# Brands & Domains
Source: https://docs.superlines.io/account/brands

Configure your brands, market settings, and competitors.

A **Brand** (also called a **Domain**) represents a distinct business entity you want to monitor in AI search. Each brand has its own name, website, target market, competitors, tracked prompts, and analytics.

## What counts as a brand?

A brand can represent many things:

* **A product line** — "Nike Running", "Nike Basketball"
* **A geographic market** — "Acme UK", "Acme Germany"
* **A business unit** — "Retail Division", "Enterprise Division"
* **A sub-brand** — "Nike", "Jordan Brand", "Converse"
* **A specific campaign or landing page**

Each brand has independent analytics and configuration. Use the **domain switcher** in the sidebar to switch between brands.

## Creating a brand

<Steps>
  <Step title="Navigate to Brand Settings and click Add Brand" />

  <Step title="Enter basic information">
    * **Brand name** — the name you want to monitor (use the most common form customers use)
    * **Website URL** — your primary URL including `https://`
    * **Business Type** — select your business model (e.g., B2B, B2C, SaaS)
    * **Description** — a brief description of your brand or business
  </Step>

  <Step title="Configure your markets">
    Add one or more markets for your brand. Each market is a combination of:

    * **Country** — the geographic market (affects prompt generation and analysis context)
    * **Language** — the language for monitoring (matches your content language)

    You can add multiple markets to monitor your brand across different regions simultaneously.
  </Step>

  <Step title="Define your audience (optional but recommended)">
    * **Target audience** — describe your ideal customer
    * **Industry / Topic** — your business category
  </Step>

  <Step title="Add competitors (up to 4 by default)">
    Enter each competitor's brand name and website. Choose direct competitors or market leaders to benchmark against.
  </Step>
</Steps>

<Warning>
  **Country and language matter.** They directly influence which prompts are generated, how responses are analyzed, and which competitors are relevant. Create separate brands for separate markets.
</Warning>

## Brand settings explained

### Brand Detection

The **Brand Detection** card in Brand Settings controls how Superlines identifies mentions of your brand in AI responses. Detection uses a two-step process:

1. **AI extraction** — an AI model reads the full response and extracts every company, brand, and product name it finds. This step is unbiased because the model doesn't know which brand you're looking for.
2. **Matching** — Superlines checks whether any of your brand names (including variations you configure) appear in the extracted list. A secondary regex-based check provides an additional signal.

If a match is found, Superlines also runs **sentiment analysis** to determine whether the mention is positive, neutral, or negative.

Brand Detection accuracy depends on two settings described below: **Brand Name Variations** (to catch all the ways your brand might be mentioned) and the **Exclude List** (to filter out false positives from similar-sounding names).

### Your Brand Name Variations

Alternative names, abbreviations, acronyms, or common misspellings that AI models might use when referring to your brand. Superlines checks all of these during brand detection, so adding variations ensures you capture every mention.

<Steps>
  <Step title="Open Brand Settings">
    Navigate to **Settings → Brand Settings** and select the brand you want to configure.
  </Step>

  <Step title="Find the Brand Detection card">
    Scroll to the **Brand Detection** section.
  </Step>

  <Step title="Add variations">
    In the **Your Brand Name Variations** field, enter comma-separated values. For example:

    `Microsoft Corp, MSFT, MS Corporation`
  </Step>

  <Step title="Save">
    Click **Save Settings** at the bottom of the page. Changes take effect on the next analysis cycle.
  </Step>
</Steps>

**What to include:**

| Type | Example (brand: "Nike Running") |
| - | - |
| Abbreviations | NRC |
| Alternate forms | Nike Run Club |
| Common misspellings | Nikee Running |
| Stock tickers / acronyms | NKE |
| Localized names | ナイキ (Japanese) |

<Tip>
  Check the **Response Analysis** page for responses where your brand is *not* detected. If you spot mentions that use an alternative name, add that name as a variation.
</Tip>

<Warning>
  Do not add generic words (e.g., "running", "cloud") as variations — they will trigger false positives across many responses.
</Warning>

### Exclude List from My Brand

Brand names that look similar to yours but should **not** be counted as your brand mentions. The exclude list filters out these names after extraction, preventing false positives in your analytics.

<Steps>
  <Step title="Open Brand Settings">
    Navigate to **Settings → Brand Settings** and select the brand you want to configure.
  </Step>

  <Step title="Find the Brand Detection card">
    Scroll to the **Brand Detection** section.
  </Step>

  <Step title="Add exclusions">
    In the **Exclude List from My Brand** field, enter comma-separated values. For example:

    `Microsoft Office, MS Paint, MS-DOS`
  </Step>

  <Step title="Save">
    Click **Save Settings**. Excluded names are filtered out starting with the next analysis cycle.
  </Step>
</Steps>

**When to use the exclude list:**

| Scenario | Your brand | Exclude |
| - | - | - |
| Parent brand vs. sub-product | Microsoft | Microsoft Office, Microsoft Teams |
| Abbreviation collision | MS (your brand) | MS Paint, MS-DOS, MS Excel |
| Similar brand name | Meta (your brand) | Meta Quest, Meta Platforms |
| Industry overlap | Apple (fruit brand) | Apple Inc, Apple Watch, iPhone |

<Info>
  Excluded names only affect **your brand's** mention counts. They do not change how competitor brands are detected or reported.
</Info>

<Tip>
  Review the **Response Analysis** page and filter for "Brand mentioned" to spot false positives. If a response is incorrectly marked as mentioning your brand because of a similar name, add that name to the exclude list.
</Tip>

### Hide from Brand Lists (Not a Brand)

AI answers sometimes name things that are not brands at all — product categories, regulators, insurance types — and the extractor stores them alongside real brands. A Finnish pension insurer, for example, will see **YEL** and **TyEL** (statutory pension insurance types) appear as "brands" next to its actual competitors.

The exclude list above does not remove these: it only stops a name from counting as *your* brand. **Hide from Brand Lists** removes the name from brand listings everywhere.

<Steps>
  <Step title="Open Brand Settings">
    Navigate to **Settings → Brand Settings** and select the brand you want to configure.
  </Step>

  <Step title="Find the Brand Detection card">
    Scroll to the **Brand Detection** section.
  </Step>

  <Step title="Add the names to hide">
    In the **Hide from Brand Lists (Not a Brand)** field, enter comma-separated values. For example:

    `YEL, TyEL, Kela`
  </Step>

  <Step title="Save">
    Click **Save Settings**. Hidden names disappear from listings on the next load (dashboard results are cached for up to two minutes).
  </Step>
</Steps>

Hidden names are removed from:

* **Brands by Brand Visibility** and the **Competitor Visibility Heatmap** in the dashboard, plus the **Competitor Analysis** and **Brand Sentiment** pages
* `analyze_brand_mentions`, `get_visibility_heatmap` and `get_competitor_insights` in the MCP server
* The `brands` endpoint of the Metrics API

Because they are removed *before* aggregation, they also stop counting towards the Share of Voice denominator, so the remaining brands' shares add up to 100% again.

<Info>
  Matching is case-insensitive on the exact name (`tyel` hides `TyEL` and `TYEL`, but not `TyEL-vakuutus`). Add each variant you see in the listing.
</Info>

### Competitor mappings

For each competitor, you can set up brand mappings to consolidate variations:

* **Canonical name** — the main name (e.g., "Nike Running")
* **Aliases** — variations (e.g., "Nike Run Club", "NRC")

This improves accuracy in Share of Voice and competitor analytics.

### Market configuration

Superlines supports **multiple markets** per brand. Each market is a country/language pair that determines how prompts are generated and responses are analyzed.

| Setting | Purpose | Impact |
| - | - | - |
| **Country** | Geographic targeting | Affects prompt generation, competitor relevance, and analysis context |
| **Language** | Content language | Determines prompt language and response analysis |
| **Region** | Optional sub-region | Further refines geographic targeting |
| **Target audience** | Customer description | Improves AI-generated prompt suggestions |

<Info>
  Unlike setting up separate brands per market, multiple markets within a single brand let you track the same brand identity across different regions while keeping analytics unified.
</Info>

### Sitemap settings

Configure automatic sitemap integration:

* **Sitemap URL** — your website's sitemap URL for automatic page discovery
* **Auto-sync** — enable to keep your tracked pages in sync with your sitemap

### Prompt automation

| Setting | Description |
| - | - |
| **Auto-Add Prompts** | Automatically add AI-discovered prompts to tracking |
| **Auto-Archive Zero-Result Prompts** | Automatically archive prompts that consistently return no brand mentions |
| **Prompt Variations** | Override the default prompt variation settings for this brand |

## Automatic prompt generation

When you create a brand, Superlines **automatically discovers relevant prompts** based on:

* Your industry and business category
* Target audience definition
* Competitor analysis
* Current trending topics

<Tip>
  The initial auto-generated prompts provide a great starting point. Review them and add your own high-priority questions as you learn more.
</Tip>

## Plan-based limits

| Resource | Limit |
| - | - |
| **Brands** | 1 on Starter, multiple on Growth, unlimited on Enterprise |
| **Competitors per brand** | 4 (default, varies by plan) |
| **Prompts** | Shared quota across all brands |

## Best practices

| Scenario | Recommendation |
| - | - |
| Single product/brand | 1 brand with core configuration |
| Multiple products | 1 brand per major product line |
| Multiple markets | 1 brand per country/language combination |
| Parent company + sub-brands | 1 brand per distinct brand identity |


# Organizations
Source: https://docs.superlines.io/account/organizations

Manage your organization settings, subscription, and AI engine configuration.

An **Organization** is the top-level container in Superlines. It holds your subscription, team members, API keys, and platform-wide configuration.

## Organization settings

Navigate to **Settings** in the sidebar to manage:

| Setting | Description |
| - | - |
| **Organization name** | Display name used in reports, invitations, and billing |
| **Website** | Your organization's primary website URL |
| **Subscription plan** | Current plan (Starter, Growth, Enterprise) |

## Subscription & usage

Your plan determines what's available across the entire organization:

| Resource | Starter | Growth | Enterprise |
| - | - | - | - |
| **Brands (domains)** | 1 | Multiple | Unlimited |
| **Tracked prompts** | 50 | 150 | Custom |
| **Countries** | 1 | Multiple | Unlimited |
| **Languages** | 1 | Multiple | Unlimited |
| **Team seats** | 1 | 3 | Custom |
| **AI engines** | 3 | 3 | All 10 |
| **Article generations** | 5 | 15 | 50 |
| **Crawled URLs** | Limited | Higher | Custom |
| **API access** | No | Yes | Yes |
| **MCP server** | No | Yes | Yes |
| **Report Builder** | No | Yes | Yes |
| **Cross-Brand Analytics** | No | No | Yes |
| **Dedicated Strategist** | No | No | Yes |

Usage is **aggregated** across all brands. For example, if Brand A uses 100 prompts and Brand B uses 50, your organization total is 150 prompts.

<Tip>
  Monitor your usage on the **Subscription** page under Settings. If you're approaching limits, consider pausing unused prompts or upgrading your plan.
</Tip>

## LLM engine configuration

Choose which AI platforms to monitor. Your plan determines how many you can enable simultaneously.

### Available engines

| Engine | Type | Plan |
| - | - | - |
| **ChatGPT** | Online + Offline | Starter+ |
| **Copilot** | Online | Starter+ |
| **Perplexity** | Online | Starter+ |
| **Google AI Overviews** | Online | Enterprise |
| **Google AI Mode** | Online | Enterprise |
| **Gemini** | Offline | Enterprise |
| **Grok** | Online | Enterprise |
| **Claude** | Offline | Enterprise |
| **Mistral** | Offline | Enterprise |
| **DeepSeek** | Offline | Enterprise |

<Info>
  **Online engines** can access current web information and tend to cite sources. **Offline engines** rely on training data and reflect historical brand authority.
</Info>

### Engine selection tips

* **Start with ChatGPT** — highest user base
* **Add Copilot** — covers Microsoft ecosystem
* **Add Perplexity** — strong for research-based queries
* **Add AI Overviews** — critical for Google search integration
* **Enterprise engines** — expand for comprehensive coverage

## API keys

API keys provide programmatic access to your Superlines data. Available on all paid plans.

<Steps>
  <Step title="Navigate to Settings → Subscription or Brand Settings to find your API Key section" />

  <Step title="Click Generate API Key">
    A new key is created with the format `sl_live_...`
  </Step>

  <Step title="Copy and store the key securely">
    Keys are shown only once at creation.
  </Step>
</Steps>

Use keys for the [REST API](/api/authentication), [MCP server](/mcp/setup), and [Looker Studio](/integrations/looker-studio).

<Warning>
  Never share API keys publicly. Store keys securely using environment variables. Rotate periodically and revoke compromised keys immediately.
</Warning>

## Permissions

| Action | Owner | Admin | Member |
| - | - | - | - |
| View settings | ✅ | ✅ | Limited |
| Edit organization details | ✅ | ❌ | ❌ |
| Manage subscription/billing | ✅ | ❌ | ❌ |
| Configure AI engines | ✅ | ✅ | ❌ |
| Generate API keys | ✅ | ❌ | ❌ |
| Delete organization | ✅ | ❌ | ❌ |


# Prompt Import
Source: https://docs.superlines.io/account/prompt-import

Import prompts from CSV files, text input, webpages, or Google Search Console.

The Prompt Import page provides multiple methods to add tracked prompts to your brand. You can upload a CSV file, paste text, extract prompts from webpages, or import keywords from Google Search Console.

## Prompt quota

At the top of the page, a quota bar shows your current prompt usage:

* **Current prompts** — how many tracked prompts you have
* **Maximum allowed** — your plan's prompt limit
* **Available** — remaining capacity

<Info>
  If you're approaching your prompt limit, consider upgrading your plan or removing inactive prompts. See [Organizations](/account/organizations) for plan details.
</Info>

## Import options

Two shared options apply to all import methods:

* **Generate AI-enhanced prompts** — uses AI to expand and refine your input into optimized tracking prompts
* **Import as raw prompts** — imports your input exactly as entered, without AI enhancement

At least one option must be selected.

## Import methods

### CSV Upload

Upload a CSV file with your prompts. The expected format includes a `prompt` column and an optional `labels` column.

<Steps>
  <Step title="Prepare your CSV">
    Create a CSV file with at least a `prompt` column. Add a `labels` column to pre-categorize prompts.
  </Step>

  <Step title="Upload the file">
    Click the upload area or drag and drop your CSV file. The file name and size will be shown.
  </Step>

  <Step title="Process">
    Click **Process CSV** to parse and validate your prompts before importing.
  </Step>
</Steps>

### Text Input

Paste or type prompts directly — one per line, or as comma-separated keywords.

<Steps>
  <Step title="Enter your prompts">
    Type or paste keywords and questions into the text area. The character count and detected prompt count are shown below.
  </Step>

  <Step title="Process">
    Click **Process Text** to parse your input into individual prompts.
  </Step>
</Steps>

### Webpage URLs

Extract prompts from webpage content. The tool analyzes the page and identifies relevant questions and topics.

<Steps>
  <Step title="Enter URLs">
    Paste one or more webpage URLs into the text area (one per line).
  </Step>

  <Step title="Configure options">
    Toggle **Include branded prompts** to include brand-specific queries extracted from the pages.
  </Step>

  <Step title="Process">
    Click **Process Webpages** to analyze the pages and extract prompts.
  </Step>
</Steps>

### Google Search Console

Import real search queries from your GSC data — these represent actual questions people ask that lead to your site.

<Steps>
  <Step title="Connect GSC">
    If not already connected, click **Go to Integrations** to set up the Google Search Console connection.
  </Step>

  <Step title="Select a date range">
    Choose how far back to fetch queries: 7 days, 14 days, 28 days, or 90 days.
  </Step>

  <Step title="Import">
    Click **Import GSC Keywords** to fetch and process your search queries.
  </Step>
</Steps>

<Tip>
  Google Search Console is one of the best sources for prompt discovery — these are real queries people type that already drive traffic to your site. Starting with a 28-day range gives a good balance of volume and relevance.
</Tip>

## Validation step

After processing any import method, you're taken to the validation page (`/prompt-import/validate`) where you can:

* Review all detected prompts before importing
* Edit individual prompts
* Remove prompts you don't want to track
* Confirm and create the prompts

## Related pages

<CardGroup>
  <Card title="Prompt Management" icon="list" href="/account/prompts">
    Manage your tracked prompts after importing.
  </Card>

  <Card title="Prompt Strategy" icon="bullseye" href="/account/prompt-strategy">
    Build a comprehensive prompt portfolio.
  </Card>

  <Card title="Google Search Console" icon="magnifying-glass" href="/integrations/google-search-console">
    Set up GSC integration for keyword import.
  </Card>

  <Card title="Prompt Radar" icon="radar" href="/account/prompt-radar">
    Auto-discover prompts from multiple data sources.
  </Card>
</CardGroup>


# Prompt Radar
Source: https://docs.superlines.io/account/prompt-radar

Automatically discover trending prompts relevant to your brand from SEO and search data sources.

**Prompt Radar** analyzes SEO, SERP, and People Also Ask (PAA) data sources to automatically surface "hot" prompts that are relevant to your brand — but not yet in your tracking.

## What it does

Prompt Radar continuously scans external search data to identify high-potential prompts you should be monitoring. It bridges the gap between traditional search intelligence and AI search monitoring.

| Data source | What Prompt Radar extracts |
| - | - |
| **SERP analysis** | Questions and queries driving search results in your industry |
| **People Also Ask** | Related questions Google surfaces for topics relevant to your brand |
| **SEO keyword data** | High-volume search queries converted into conversational AI prompts |

<Info>
  Prompt Radar finds prompts based on your brand's industry, target market, and existing prompt portfolio. The more context your brand has, the more relevant the discoveries.
</Info>

## How it works

<Steps>
  <Step title="Superlines analyzes your brand context">
    Based on your brand's industry, competitors, target audience, and existing tracked prompts, the system understands your market space.
  </Step>

  <Step title="External search data is scanned">
    SEO, SERP, and PAA data sources are analyzed to find trending or high-volume queries related to your space.
  </Step>

  <Step title="Prompts are scored and filtered">
    Discovered queries are converted into conversational prompts and scored for relevance to your brand. Duplicates and near-duplicates of your existing prompts are filtered out.
  </Step>

  <Step title="Opportunities are surfaced">
    Relevant prompts appear as **Prompt Opportunities** on your Dashboard, ready to add with one click.
  </Step>
</Steps>

## Viewing Prompt Radar suggestions

Prompt Radar suggestions appear in the **Dashboard** under the **Prompt Opportunities** section. Each suggestion includes:

* The prompt text
* The data source it was discovered from
* A relevance indicator
* A one-click action to add it to tracking

## When to use Prompt Radar

Prompt Radar is especially valuable when:

* **You're starting out** and need to build your prompt portfolio quickly
* **You don't have extensive internal data** (FAQ, support tickets, keyword lists) to draw from
* **You want to discover trends** — new topics emerging in your market that you haven't considered
* **You want continuous discovery** — markets evolve, and new questions emerge as AI search patterns change

<Tip>
  Even if you have strong internal data, Prompt Radar catches market shifts you might miss. Use it as a complementary signal alongside your own data sources. See the [Prompt Strategy](/account/prompt-strategy) guide for how to combine multiple sources.
</Tip>

## Prompt Radar vs. other discovery methods

| Method | Best for | Effort | Coverage |
| - | - | - | - |
| **Prompt Radar** | Trending prompts, new topics, market shifts | Automatic | SERP, PAA, SEO data |
| **Google Search Console** | Queries already driving traffic to your site | Low (one-time integration) | Your existing search presence |
| **Bulk import from internal data** | Known customer questions, FAQ, support themes | Medium (data preparation) | Your existing knowledge base |
| **MCP-powered discovery** | Custom workflows, external data sources, automated pipelines | Higher (setup required) | Any data source you connect |
| **Manual addition** | Specific strategic prompts you've identified | Low per prompt | Your expertise |

## Best practices

| Practice | Why |
| - | - |
| **Review suggestions weekly** | New prompts surface as market data updates |
| **Add selectively** | Not every suggestion is strategic — focus on prompts aligned with your goals |
| **Label discovered prompts** | Tag Prompt Radar discoveries separately (e.g., `radar-discovery`) to measure their impact over time |
| **Combine with variations** | Enable [prompt variations](/account/prompt-variations) on radar-discovered prompts to test phrasing sensitivity |
| **Validate with volume data** | Cross-reference radar suggestions with keyword volume using the [keyword research tool](/mcp/tools#keyword_research) |


# Prompt Strategy
Source: https://docs.superlines.io/account/prompt-strategy

Build a comprehensive prompt portfolio using multiple data sources and strategies.

Choosing the right prompts to track is the most important step in Superlines. Your prompt portfolio defines the context for all your AI search visibility data — get it right, and every metric becomes actionable.

## Why prompt selection matters

Unlike traditional SEO where you optimize for keywords with known search volume, AI search prompts are conversational and varied. The exact phrasing matters less than the intent — AI engines understand nuance. What matters is tracking prompts that represent real questions your market is asking.

<Info>
  Enable [prompt variations](/account/prompt-variations) to automatically test different phrasings of the same question. This ensures your tracking isn't dependent on exact wording.
</Info>

## Data sources for prompt creation

The best prompt portfolios combine multiple data sources. Here's how to use each one:

### 1. Your own business data

If your organization has existing marketing, customer success, or content data, use it as the foundation. This is the most reliable source because it reflects your actual market.

| Source | How to use it | Example |
| - | - | - |
| **Marketing messages** | Convert your value propositions and messaging pillars into questions a prospect would ask | "What is the easiest way to get YEL insurance?" |
| **FAQ databases** | Import existing FAQ questions directly — these are proven real customer questions | "How do I cancel my subscription?" |
| **Support ticket themes** | Extract recurring questions from support data | "Why isn't my integration syncing?" |
| **Sales objections** | Frame common objections as research questions | "Is \[product] worth the price for small teams?" |
| **Internal keyword research** | Convert high-volume keywords into conversational prompts | Keyword "CRM startups" becomes "What is the best CRM for startups?" |

<Tip>
  You don't need to match exact phrasing. AI engines understand intent. A prompt like "Best CRM for startups" will capture responses whether a user asks "What CRM should I use for my startup?" or "Which CRMs work well for early-stage companies?"
</Tip>

### 2. Google Search Console

The [Google Search Console integration](/integrations/google-search-console) imports real search queries that already drive impressions or clicks to your site. These are proven high-relevance queries.

**Best practice:** Focus on queries with high impressions but lower clicks — these represent topics where users are searching but may be getting AI-powered answers instead of clicking through.

### 3. Prompt Radar

[Prompt Radar](/account/prompt-radar) automatically discovers trending prompts from SEO, SERP, and People Also Ask data sources. It finds "hot" prompts that are relevant to your brand but not yet in your tracking.

### 4. Long-tail keyword data + AI generation

Combine traditional keyword research with AI to generate high-intent prompts for the markets and products you already track:

1. **Start with keyword data** — use tools to find long-tail keywords with real search volume in your niche
2. **Convert to conversational prompts** — use the Superlines [keyword research tool](/mcp/tools#keyword_research) or an LLM to transform keywords into natural-language questions
3. **Validate relevance** — ensure the generated prompts match your brand's positioning and audience
4. **Import and label** — add prompts via [bulk import](/account/prompts#bulk-import) or [MCP](/mcp/tools#add_prompts) with appropriate labels

<Warning>
  Avoid tracking prompts that have zero market relevance. Even if a prompt seems interesting, it only matters if real users are asking that question to AI engines. Use keyword volume data and your own customer data as validation signals.
</Warning>

### 5. MCP-powered prompt discovery

Use the [MCP server](/mcp/workflows#prompt-discovery) to build automated prompt discovery workflows:

* Scrape trending articles or SERP results with an external tool
* Use an LLM to extract relevant questions
* Add them to Superlines via `add_prompts` automatically
* Label and categorize for organized tracking

### 6. Competitor-inspired prompts

Analyze where competitors have strong AI visibility using [competitive gap analysis](/metrics/competitive-intelligence). Their strengths reveal prompts your market cares about that you may be missing.

***

## Organizing your prompt portfolio

### Labels for structure

Use [labels](/account/prompts#labels) to organize prompts into meaningful categories:

| Label strategy | Examples | Use case |
| - | - | - |
| **By funnel stage** | `awareness`, `consideration`, `decision` | Track visibility across the customer journey |
| **By topic** | `pricing`, `features`, `comparison` | Identify strong and weak topic areas |
| **By priority** | `strategic`, `monitoring`, `experimental` | Allocate effort where it matters most |
| **By team** | `brand`, `content`, `pr` | Enable team-specific dashboards |
| **By campaign** | `q1-launch`, `summer-promo` | Measure campaign-specific AI visibility |

### Strategic vs. monitoring prompts

Not all prompts are equal. Classify them:

* **Strategic prompts** — high-value questions where you *must* beat competitors. Monitor these closely, invest in content optimization, and track week-over-week changes.
* **Monitoring prompts** — broader questions that give you market awareness. Track them for trends but don't invest heavily in each one.
* **Experimental prompts** — new or unvalidated questions you're testing. Use [Prompt Radar](/account/prompt-radar) to discover these, evaluate for 2-4 weeks, then promote or archive.

***

## Prompt portfolio health checklist

<AccordionGroup>
  <Accordion title="Are your prompts representative of your market?">
    Your prompts should cover the full range of questions your target audience asks — not just the ones you want to rank for. Include comparison prompts, problem-solving prompts, and brand-specific prompts.
  </Accordion>

  <Accordion title="Are you using multiple data sources?">
    A healthy portfolio combines business data, search data, automated discovery (Prompt Radar), and competitive insights. Relying on a single source creates blind spots.
  </Accordion>

  <Accordion title="Are your prompts labeled and organized?">
    Without labels, your data becomes noise. Organize by topic, priority, funnel stage, or team to make analytics actionable.
  </Accordion>

  <Accordion title="Are you refreshing your prompts regularly?">
    Markets evolve. Set up [Prompt Radar](/account/prompt-radar) to continuously discover new relevant prompts, and periodically review and pause low-value ones to free quota.
  </Accordion>

  <Accordion title="Are variations enabled?">
    [Prompt variations](/account/prompt-variations) ensure your visibility data isn't skewed by specific phrasing. Enable at least a 25% variation rate.
  </Accordion>
</AccordionGroup>


# Prompt Variations
Source: https://docs.superlines.io/account/prompt-variations

Control how prompt variations are used during crawling and testing to understand how LLMs respond to different phrasings.

**Prompt variations** are rephrased versions of your tracked prompts. When enabled, Superlines will occasionally use a slightly different wording of the same question during crawls — helping you understand how sensitive AI responses are to phrasing.

## Why use prompt variations?

AI engines don't always respond the same way to different phrasings of the same question. For example:

| Original prompt | Variation |
| - | - |
| *"What is the best CRM for startups?"* | *"Which CRM tools are recommended for startup companies?"* |
| *"Best running shoes 2025"* | *"Top-rated running shoes this year"* |

Prompt variations help you:

* **Measure phrasing sensitivity** — see if your brand visibility changes with different wordings
* **Discover blind spots** — find phrasings where competitors outperform you
* **Improve coverage** — understand how LLMs interpret different versions of similar queries
* **Get more robust data** — reduce the impact of any single phrasing on your metrics

## How it works

When a crawl is triggered for a prompt:

1. The system checks if prompt variations are **enabled** for the brand (or organization)
2. Based on the configured **variation rate**, a random check determines whether to use a variation
3. If a variation is selected, the system either picks from **existing variations** or **generates a new one** using AI
4. The response is analyzed and tagged as a variation in your analytics

<Info>
  Variation results are clearly tagged in your data, so you can always distinguish between original prompt results and variation results.
</Info>

## Configuration hierarchy

Prompt variation settings follow a hierarchy:

| Level | Priority | Description |
| - | - | - |
| **Brand (domain)** | Highest | Overrides organization settings for a specific brand |
| **Organization** | Default | Applies to all brands unless overridden |

If a brand has its own variation settings configured, those are used. Otherwise, the organization-level settings apply.

## Configuring at the organization level

Organization-level settings apply as the default for all brands.

<Steps>
  <Step title="Navigate to Organization Settings" />

  <Step title="Find the Prompt Variations section" />

  <Step title="Toggle Enable Prompt Variations">
    Turn on to allow variations across all brands in your organization.
  </Step>

  <Step title="Set the Variation Rate">
    Use the slider to set the percentage (5%–100%). For example, at **25%**, roughly 1 in 4 crawls will use a rephrased prompt instead of the original.
  </Step>

  <Step title="Save your settings" />
</Steps>

<Tip>
  A **25% variation rate** is a good starting point — it gives you meaningful variation data while keeping the majority of crawls on your original prompts.
</Tip>

## Configuring at the brand level

You can override the organization default for individual brands. This is useful when:

* You want to **test variations** on one brand before rolling out to all
* A specific brand needs a **higher or lower** variation rate
* You want to **disable variations** for a particular brand while keeping them on elsewhere

<Steps>
  <Step title="Navigate to Brand Settings for the specific brand" />

  <Step title="Find the Prompt Variations section" />

  <Step title="Enable Override Organization Setting">
    This activates brand-specific variation controls.
  </Step>

  <Step title="Toggle Enable Prompt Variations and set the rate" />

  <Step title="Save your settings" />
</Steps>

<Note>
  If you disable the override, the brand will revert to using the organization-level settings.
</Note>

## Defaults

| Scenario | Variations enabled | Variation rate |
| - | - | - |
| **New organizations** | Off | 25% (pre-configured for when you enable) |
| **New brands** | Inherits from organization | — |
| **Existing organizations** (before this feature) | On | 25% |

Existing organizations retain their previous behavior (variations active at 25%) to avoid disrupting ongoing tracking. New organizations start with variations **disabled** so you can opt in when ready.

## Viewing variation results

Variation results appear throughout your analytics:

* **Response Analysis** — variation responses are tagged with a variation indicator
* **Prompt detail view** — see results for both original and variation phrasings
* **Exports and API** — variation metadata is included in data exports

## Best practices

| Tip | Details |
| - | - |
| **Start at 25%** | Enough data for insights without overwhelming your original prompt tracking |
| **Test on one brand first** | Use brand-level overrides to pilot before organization-wide rollout |
| **Review after 2–4 weeks** | Allow enough crawl cycles to gather meaningful variation data |
| **Compare original vs variation** | Look for significant differences in brand visibility or citations |
| **Increase rate for research** | Temporarily boost to 50–75% when doing deep phrasing analysis |

## FAQ

<AccordionGroup>
  <Accordion title="Do variations count toward my prompt quota?">
    No. Variations are generated automatically from your existing tracked prompts. They do not create new prompt entries or affect your quota.
  </Accordion>

  <Accordion title="Can I see which variation was used for a specific response?">
    Yes. Each response in the analytics is tagged with whether it used the original prompt or a variation, along with the exact variation text.
  </Accordion>

  <Accordion title="How are variations generated?">
    The system uses AI to create natural rephrasings that preserve the original intent. It may also reuse previously generated variations to ensure consistency over time.
  </Accordion>

  <Accordion title="Will enabling variations change my existing metrics?">
    Variation results are tracked separately from original prompt results. Your existing metrics remain unaffected. When viewing aggregated data, both original and variation results contribute to the totals.
  </Accordion>

  <Accordion title="Can I edit or delete individual variations?">
    Currently, variations are managed automatically by the system. You control whether they are enabled and at what rate, but individual variation texts are generated and selected by the AI.
  </Accordion>
</AccordionGroup>


# Prompt Management
Source: https://docs.superlines.io/account/prompts

Add, organize, and manage the prompts you track in Superlines.

**Prompts** are the questions people ask AI engines. Superlines tracks these prompts to measure your brand's visibility in responses.

## Adding prompts

There are several ways to add prompts to tracking:

### Auto-generated prompts

When you create a brand, Superlines automatically discovers relevant prompts based on your industry, target audience, and competitors. Review these suggestions and accept the ones that matter.

### Manual addition

1. Go to **AI Search Visibility → Prompts** tab
2. Click **Add Prompts**
3. Enter one or more questions (e.g., *"What is the best CRM for startups?"*)
4. Confirm to create the prompts

### Bulk import

Navigate to **Prompt Import** to:

* Upload a **CSV file** with prompts
* **Paste prompts** as text (comma or line-separated)
* Extract prompts from **Webpage URLs**
* Import from **Google Search Console**

### Prompt opportunities

The **Dashboard** shows **Prompt Opportunities** — AI-discovered prompts relevant to your brand that you can add with one click. These are powered by [Prompt Radar](/account/prompt-radar), which automatically discovers trending prompts from SEO, SERP, and People Also Ask data sources.

### MCP-powered prompt management

You can also add and manage prompts programmatically via the [MCP server](/mcp/tools#add_prompts). This enables automated workflows where AI agents discover prompts from external data sources and add them to tracking. See [MCP Automation Workflows](/mcp/workflows#prompt-discovery) for examples.

<Info>
  For a comprehensive guide on choosing the right prompts from multiple data sources — including marketing data, FAQ databases, keyword research, and automated discovery — see the [Prompt Strategy](/account/prompt-strategy) guide.
</Info>

## Prompt statuses

| Status | Meaning |
| - | - |
| ✅ **Active** | Being tested regularly across AI engines |
| ⏳ **Processing** | Recently added, being queued for testing |
| ⏸️ **Paused** | Temporarily stopped — frees quota |
| 🚫 **Ignored** | Excluded from tracking |

<Tip>
  **Pause** low-value prompts instead of deleting them. This frees quota while preserving historical data. You can resume anytime.
</Tip>

## Labels

Labels are tags you can assign to prompts for organization:

* Group prompts by **topic** or **campaign**
* Filter analytics by **priority** or **funnel stage**
* Track prompts for specific **product lines**

Labels can be **manually assigned** or **AI-generated** (Superlines can auto-label based on content analysis).

### Auto-labeling

Enable prompt automation in settings to:

* **Auto-add** suggested prompts from discovery
* **Auto-label** prompts based on content analysis
* **Auto-cleanup** duplicate or near-duplicate prompts

## Prompt quotas

Your organization has a **prompt quota** that's shared across all brands:

| Plan | Prompt quota |
| - | - |
| Starter | 50 |
| Growth | 150 |
| Enterprise | Custom |

Prompts in **Active** or **Processing** status count toward your quota. **Paused** prompts do not.

<Note>
  Check your current usage on the **Subscription** page under Settings. If you're near the limit, pause low-performing prompts or upgrade your plan.
</Note>

## Deleting prompts

Deleted prompts are permanently removed and free up quota immediately. Historical data is retained for reporting purposes.

To delete: select prompts in the table and use the bulk action menu, or delete individually from the prompt detail view.

## Where to find prompts, citations, and responses

| What | Where |
| - | - |
| **Tracked prompts** | Dashboard → AI Search Visibility |
| **Prompt details** | Click any prompt in the table |
| **Full AI responses** | Response Analysis page |
| **Citation details** | Citations Analysis page |
| **Prompt performance trends** | Prompt detail view → Trend chart |


# Subscription & Billing
Source: https://docs.superlines.io/account/subscription

Manage your plan, monitor usage, and understand feature availability.

The Subscription page shows your current plan, usage across key dimensions, and the features and AI engines available on your plan.

## Current plan

The top of the page displays your active plan details:

* **Plan name** — Starter, Growth, or Enterprise
* **Billing cycle** — monthly or yearly
* **Status** — active, trial, or cancelled
* **Next billing date** — when your next payment is due
* **Cancellation date** — shown if the plan is set to cancel

## Available plans

Browse all available plans with their features and pricing. Each plan card shows:

* Plan name and description
* Key features included
* Pricing (monthly and yearly options)
* Call-to-action to upgrade or switch

<Info>
  Yearly billing typically offers a discount compared to monthly billing. You can switch between billing cycles from the subscription management portal.
</Info>

## Usage monitoring

Track your current usage against plan limits:

| Resource | What it tracks |
| - | - |
| **User Seats** | Team members in your organization |
| **Tracked Prompts** | Active prompts across all domains |
| **Enabled LLM Engines** | AI engines activated for monitoring |
| **Article Generations** | Monthly content generation quota |
| **Crawled URLs** | Pages monitored for site health (org-wide) |

Each resource shows a progress bar indicating current usage vs. your plan limit. Warnings appear when you're approaching or exceeding limits.

<Warning>
  When you exceed a usage limit, related features may be restricted. For example, exceeding your tracked prompt limit prevents adding new prompts until you remove existing ones or upgrade.
</Warning>

## Features by plan

The Features section shows which capabilities are available on your current plan:

* Report Builder
* Industry Data
* Cross-Brand Analytics
* CSV Export
* And other plan-specific features

Features not available on your current plan are shown as locked, with an indication of which plan includes them.

## LLM engines

View all available AI engines and which ones are currently enabled for your organization. The number of engines you can enable depends on your plan tier.

## Managing your subscription

Click **Manage Subscription** to open the customer portal where you can:

* Change your plan (upgrade or downgrade)
* Switch billing cycle (monthly ↔ yearly)
* Update payment methods
* View billing history and invoices
* Cancel your subscription

## Plan limits reference

For a complete reference of plan tiers, feature limits, and engine availability, see the [Organizations](/account/organizations) documentation.

<CardGroup>
  <Card title="Organizations" icon="building" href="/account/organizations">
    Plan tiers, limits, and organization settings.
  </Card>

  <Card title="Team & Roles" icon="users" href="/account/team">
    Manage team members within your plan's seat limit.
  </Card>
</CardGroup>


# Team & Roles
Source: https://docs.superlines.io/account/team

Invite team members and manage roles and permissions.

Superlines supports role-based access control so your whole team can collaborate with the right level of access.

## Roles

| Role | Description |
| - | - |
| **Owner** | Full platform control including billing, organization management, and team administration. Can delete the organization. |
| **Admin** | Full analytics and monitoring access. Can manage team members (except owners), configure brands, and manage settings. Cannot access billing. |
| **Member** | Read-only access to analytics, dashboards, and reports. Cannot modify settings or invite others. |

### Detailed permissions

| Action | Owner | Admin | Member |
| - | - | - | - |
| View analytics & dashboards | ✅ | ✅ | ✅ |
| Export data | ✅ | ✅ | ✅ |
| Manage prompts | ✅ | ✅ | ❌ |
| Configure brand settings | ✅ | ✅ | ❌ |
| Invite team members | ✅ | ✅ | ❌ |
| Remove team members | ✅ | ✅\* | ❌ |
| Change member roles | ✅ | ❌ | ❌ |
| Manage billing & subscription | ✅ | ❌ | ❌ |
| Generate API keys | ✅ | ❌ | ❌ |
| Delete organization | ✅ | ❌ | ❌ |

\*Admins can remove Members but not other Admins or Owners.

## Inviting members

<Steps>
  <Step title="Navigate to Team Management" />

  <Step title="Click Invite Member" />

  <Step title="Enter their email address" />

  <Step title="Select a role: Admin or Member" />

  <Step title="Click Send Invitation">
    The invitee receives an email with a sign-up/sign-in link. Once they accept, they're automatically added to your organization.
  </Step>
</Steps>

<Tip>
  Only **Owners** and **Admins** can send invitations. Members cannot invite others.
</Tip>

## Organization vs. brand-level members

The Team Management page has two separate sections:

### Organization Members

Members invited at the organization level have access based on their role (Owner, Admin, Member) across the organization. Use the **Invite Member** button in this section to add organization-wide team members.

### Brand-level Members

You can also invite members to a **specific brand** using the **Invite to Brand** button. Brand-level members only have access to the brand they were invited to, making this useful for:

* **Agencies** managing multiple client brands where each client should only see their own data
* **Enterprises** with distinct teams per brand or business unit
* **External consultants** who need access to a single brand

Brand-scoped members appear in a separate "Members in Brand: \[BrandName]" section on the Team Management page.

## Seat limits

| Plan | Default seats |
| - | - |
| Starter | 1 |
| Growth | 3 |
| Enterprise | Custom |

If you've reached your seat limit, remove inactive members or upgrade your plan.

## Best practices

| Team type | Recommended structure |
| - | - |
| **Marketing team** | Marketing Manager (Admin), Content Team (Members), CMO (Owner) |
| **Agency** | Agency Lead (Admin), Account Managers (Members), Client Stakeholders (Members) |
| **Enterprise** | IT Admin (Owner), Regional Managers (Admins), Analysts (Members) |


# Brand Sentiment
Source: https://docs.superlines.io/analytics/brand-sentiment

Understand how AI models perceive and present your brand across engines.

The Brand Sentiment page shows how AI models talk about your brand — whether responses are positive, neutral, or negative. It goes beyond simple mention counting to analyze the tone and context of every AI response that references your brand.

## Brand-specific analysis

The primary view focuses on sentiment extracted specifically about your brand from AI responses.

### Overview metrics

| Metric | Description |
| - | - |
| **Total Analyses** | Number of brand-specific sentiment extractions performed |
| **Positive Count** | Responses where your brand is presented favorably |
| **Negative Count** | Responses where concerns are raised about your brand |
| **Neutral Count** | Factual mentions without strong sentiment |
| **Positive %** | Percentage of positive mentions, with change vs. previous period |

### Sentiment distribution

A visual bar shows the breakdown of positive, neutral, and negative mentions at a glance. The trend chart below tracks how this distribution changes over time — useful for spotting shifts in how AI models perceive your brand.

### Top mentions

See the actual AI-generated text snippets that mention your brand, categorized as positive or negative. Each snippet shows which engine produced it and which tracked prompt triggered it. This gives you direct visibility into what AI models are saying.

### Brand sentiment by theme

Understand how your brand is perceived across different conversation themes. Each theme shows:

* Sentiment breakdown (positive, negative, neutral percentages)
* Associated keywords
* Theme name and topic area

Filter themes by "Mostly Positive", "Mostly Negative", "Mixed", or search for specific themes.

## Competitor sentiment comparison

A comparison table showing how your brand's sentiment stacks up against competitors:

| Column | Description |
| - | - |
| **Brand** | Brand name |
| **Total Mentions** | How often this brand is mentioned |
| **Avg Position** | Average mention position in AI responses |
| **Sentiment Distribution** | Visual bar of positive/neutral/negative |
| **Positive %** | Percentage of positive mentions |
| **Negative %** | Percentage of negative mentions |
| **Avg Sentiment Score** | Numerical sentiment score |

Toggle **Show all brands** to see all detected brands beyond your configured competitors.

## Overall response analysis

A secondary view that analyzes the general tone of all AI responses — not just brand-specific mentions. This helps you understand the broader conversation context around your tracked prompts.

Metrics include total responses, brand mentions count, sentiment distribution (shown as a donut chart), and a response sentiment trend chart over time.

## Filters

* **Date range** — select the analysis period
* **LLM engines** — filter by individual engines (ChatGPT, AI Mode, AI Overviews, etc.) using engine buttons
* **Prompt labels** — filter by prompt categories (multi-select)

<Tip>
  Monitor sentiment trends weekly. A gradual shift from positive to neutral might indicate that competitors are gaining ground in AI responses, or that AI models are updating their training data with new information about your brand.
</Tip>

## Recent brand mentions

A live feed of recent AI responses mentioning your brand, showing:

* The response snippet with sentiment indicator
* Which AI engine generated it
* Which prompt triggered it
* The date

Filter by sentiment (All, Positive, Neutral, Negative) and click any mention to open a detailed view with the full response context, model used, position, topic, and cited URLs.

## Related pages

<CardGroup>
  <Card title="Key Metrics" icon="chart-bar" href="/metrics/key-metrics">
    How sentiment score is calculated.
  </Card>

  <Card title="Mentions" icon="message" href="/analytics/mentions">
    Explore all brand mentions across AI responses.
  </Card>

  <Card title="Competitor Analysis" icon="users" href="/analytics/competitor-analysis">
    Compare competitor performance on all metrics.
  </Card>

  <Card title="Understanding Responses" icon="file-lines" href="/metrics/responses">
    How AI responses are collected and analyzed.
  </Card>
</CardGroup>


# Citations Analysis
Source: https://docs.superlines.io/analytics/citations-page

Track which URLs are cited in AI responses and find opportunities to earn more citations.

The Citations Analysis page shows when and where AI models cite your website (and competitors') as a source. Citations are a strong signal of content authority — they mean an AI model considered your content authoritative enough to link to.

## Overview tab

### Citation Rankings

A ranked table showing how domains perform on citations:

* Domain rank by total citations
* Citation count and percentage share
* Change vs. previous period
* Filter between "My Domain", "Competitors", and "Industry" views

### Citations Gap

Identifies prompts where competitor URLs are cited but yours are not. Each gap shows:

* The prompt where you're missing citations
* Which competitor domains are being cited
* The specific URLs being cited
* The opportunity size

<Tip>
  The Citations Gap is your roadmap for content optimization. Each entry shows a specific prompt where a competitor's URL is cited — study that URL to understand what makes it citation-worthy, then create or improve your own content to compete.
</Tip>

## UGC & Community tab

Identifies citation opportunities from user-generated content platforms where AI models frequently source information:

* **Reddit** — subreddit discussions being cited
* **Quora** — Q\&A threads being cited
* **YouTube** — video content being cited
* Other community platforms

These opportunities highlight where participating in community discussions could lead to AI citations.

## Citations Data tab

A comprehensive table listing individual citation records:

| Column | Description |
| - | - |
| **LLM Service** | Which AI engine produced the response |
| **Prompt** | The tracked prompt that triggered the response |
| **Cited Status** | Whether your domain was cited |
| **Domain** | The cited domain |
| **Page Title** | Title of the cited page |
| **Page URL** | Full URL of the cited page |
| **Country** | Geographic context |
| **Date** | When the response was generated |
| **Sentiment** | Sentiment of the response |

Click any row to open a detail modal showing:

* Full prompt text
* AI response snippet with citation context
* Which engine generated the response
* All cited URLs in the response
* Citation position

## Filters

### Basic filters

* **Date range** — select the analysis period
* **LLM engines** — filter by specific AI engines
* **Prompt** — focus on a specific tracked prompt
* **Prompt tags** — filter by prompt categories

### Advanced filters

Click **More filters** to access:

* **Citation Status** — cited only or not cited
* **AI Engine** — single engine selection
* **Country** — geographic filter
* **Topic** — content topic filter
* **Prompt Tag** — prompt category filter
* **My domain only** — show only your domain's citations
* **Competitors only** — show only competitor citations
* **Search by Domain** — find citations from a specific domain
* **Search by Title** — search cited page titles

## Exporting data

The Citations Data tab supports CSV export for all filtered citation records.

## Related pages

<CardGroup>
  <Card title="Citations & Fan-out Queries" icon="link" href="/metrics/citations">
    How citations are tracked and what fan-out queries are.
  </Card>

  <Card title="Mentions" icon="message" href="/analytics/mentions">
    Explore brand mentions alongside citation data.
  </Card>

  <Card title="Content Module" icon="file-lines" href="/content/overview">
    Create content to earn more citations.
  </Card>

  <Card title="Key Metrics" icon="chart-bar" href="/metrics/key-metrics">
    How citation rate and share of citations are calculated.
  </Card>
</CardGroup>


# Competitor Analysis
Source: https://docs.superlines.io/analytics/competitor-analysis

Compare your brand's AI search performance against competitors across all key metrics.

The Competitor Analysis page provides a dedicated view for benchmarking your brand against competitors in AI search. See who's winning on visibility, citations, and share of voice — and identify specific prompts where you can close the gap.

## KPI summary

Four cards at the top give you an instant competitive overview:

| Card | What it shows |
| - | - |
| **Your Brand Visibility** | Your brand visibility percentage with trend |
| **Competitors Tracked** | How many competitors are being monitored |
| **Top Competitor BV** | The competitor with the highest brand visibility |
| **Avg Competitor BV** | Average brand visibility across all tracked competitors |

## Brand Performance Overview

The main table ranks all brands (yours and competitors) by performance:

| Column | Description |
| - | - |
| **Rank** | Position based on overall brand visibility |
| **Brand** | Brand name with logo |
| **Top Fan-out Query** | The LLM-generated search query where this brand performs best |
| **Top Cited URL** | The URL most frequently cited for this brand |
| **Brand Visibility** | Percentage of AI responses mentioning this brand |
| **Total Mentions** | Absolute count of brand mentions |
| **Total Citations** | Number of times this brand's URLs were cited |

Click any row to open a detailed modal with deeper insights into that brand's performance by engine, prompt, and more.

## Charts and market share

### Brand Visibility Comparison

A bar chart comparing Brand Visibility percentages across all brands. This makes it easy to see the relative gap between your brand and competitors at a glance.

### Market Share by Share of Voice

A donut chart showing how total brand mentions are distributed. Your Share of Voice tells you what percentage of the conversation your brand owns compared to competitors.

### Share of Citations

A donut chart showing how website citations are distributed across brands. This reveals whose content AI models consider most authoritative.

<Tip>
  Click on chart segments to open detailed modals. For example, clicking a competitor's bar in the visibility chart shows their top-performing prompts and engines.
</Tip>

## Competitor top-performing prompts

A table showing prompts where competitors outperform you:

| Column | Description |
| - | - |
| **Competitor** | Which competitor leads on this prompt |
| **Their Top Prompt** | The prompt text |
| **Their BV** | The competitor's brand visibility on this prompt |
| **Your BV** | Your brand visibility on the same prompt |
| **Gap** | The difference — your opportunity |

This is one of the most actionable sections. Prompts with a large gap represent specific topics where targeted content can improve your visibility.

## Filters

* **Date range** — select the analysis period
* **LLM engines** — filter by specific AI engines (multi-select)
* **Prompt labels** — focus on specific prompt categories
* **Specific prompt** — drill into a single tracked prompt
* **Show all brands** — toggle between configured competitors only or all detected brands
* **Competitor selection** — when "Show all brands" is off, choose which competitors to compare

## Exporting data

Export options are available on the Brand Performance table and charts:

* **CSV** — tabular data export
* **JSON** — structured data export
* **PNG** — chart image export for presentations

## Related pages

<CardGroup>
  <Card title="Cross-Brand Analytics" icon="chart-pie" href="/analytics/cross-brand-analytics">
    Compare performance across multiple brands you manage.
  </Card>

  <Card title="Key Metrics" icon="chart-bar" href="/metrics/key-metrics">
    Understand how Brand Visibility and SOV are calculated.
  </Card>

  <Card title="Mentions" icon="message" href="/analytics/mentions">
    Deep-dive into response data and mention rankings.
  </Card>

  <Card title="Brands & Domains" icon="globe" href="/account/brands">
    Configure your competitors and brand settings.
  </Card>
</CardGroup>


# Cross-Brand Analytics
Source: https://docs.superlines.io/analytics/cross-brand-analytics

Compare AI search performance across multiple brands and domains in a single view.

Cross-Brand Analytics compares performance across multiple brands or domains in one organization. Enterprise marketing teams use it for centralized reporting across brands, markets, languages and business units. Agencies use it to report across a client portfolio, then drill into a single brand.

## Overview

Select any combination of your brands to see side-by-side performance data. The page aggregates metrics across all selected domains and provides comparative views for brand visibility, share of voice, citation rate, and more.

## Aggregated metrics

Six metric cards summarize performance across your selected domains:

| Metric | Description |
| - | - |
| **Total Domains** | Number of domains selected for comparison |
| **Tracked Prompts** | Total prompts tracked across all selected domains |
| **Total Tests/Mentions** | Combined test count and mention volume |
| **Avg Brand Visibility** | Average brand visibility across selected domains |
| **Avg Share of Voice** | Average SOV across selected domains |
| **Avg Citation Rate** | Average citation rate across selected domains |

## Domain Performance Table

The main table compares each selected domain on key metrics:

| Column | Description |
| - | - |
| **Domain** | The brand/domain name |
| **Brand Visibility** | Percentage of AI responses mentioning this brand |
| **Share of Voice** | This brand's share of all brand mentions |
| **Citation Rate** | Percentage of responses citing this brand's URLs |
| **Total Mentions** | Absolute mention count |
| **Total Citations** | Absolute citation count |
| **Sentiment Score** | Overall sentiment of AI responses about this brand |

Click any row to open a detailed modal with per-prompt and per-engine breakdowns for that domain.

## Analytics sections

### Prompt Opportunities

Lists prompts where a domain has low brand visibility but high search volume — these represent the biggest opportunities for improvement. Each opportunity shows the prompt, current visibility, and potential impact.

### Top Cited Domains

Shows which domains (yours and others) are cited most across AI responses for your tracked prompts. This reveals the competitive landscape for citations.

### Trends

Charts showing brand visibility, share of voice, and citation rate trends over time for all selected domains. Overlay multiple domains to compare trajectory and identify divergence points.

### Top Prompts & Weak Prompts

* **Top Prompts** — prompts with the highest brand visibility across selected domains
* **Weak Prompts** — prompts with the lowest brand visibility, highlighting areas that need attention

### Brand Visibility by AI Engine

Breakdown of how each domain performs on different AI engines. This reveals engine-specific strengths and weaknesses — for example, one brand might perform well on ChatGPT but poorly on Perplexity.

### Performance by category

Additional breakdowns by:

* **Industries** — visibility by industry classification
* **Countries** — geographic performance differences
* **Target Groups** — performance by audience segment
* **Topics** — visibility across content topics

## Filters

* **Date range** — select the analysis period
* **Domain selector** — choose one or more brands to compare (multi-select)
* **LLM engines** — filter by specific AI engines
* **Advanced filters** — filter by country, language, industry, topic, business type, and target group

<Tip>
  For agency teams: use Cross-Brand Analytics for monthly client reporting. Select each client's domain to generate a portfolio-level view, then drill into individual domains for client-specific insights.
</Tip>

## Detail modals

Click on metrics, table rows, or chart segments to open detailed modals:

* **Engine Brands** — top brands for a specific AI engine
* **Brand Prompts** — top prompts for a specific brand
* **Prompt Brands** — top brands for a specific prompt
* **Cited Domain URLs** — specific URLs cited from a selected domain

## Related pages

<CardGroup>
  <Card title="Competitor Analysis" icon="users" href="/analytics/competitor-analysis">
    Single-brand competitor benchmarking.
  </Card>

  <Card title="Brands & Domains" icon="globe" href="/account/brands">
    Manage your brand and domain configurations.
  </Card>

  <Card title="Organizations" icon="building" href="/account/organizations">
    Manage your organization and plan settings.
  </Card>

  <Card title="Export & Reporting" icon="file-export" href="/features/export-reporting">
    Export cross-brand data for stakeholder reporting.
  </Card>
</CardGroup>


# Mentions
Source: https://docs.superlines.io/analytics/mentions

Explore how your brand is mentioned across AI responses and identify gaps where competitors lead.

The Mentions page (Response Analysis) provides a complete view of your brand's presence in AI-generated responses. It combines a high-level overview of mention share and rankings with a detailed table of every tracked response.

## Overview tab

### Share of Mentions

A donut chart showing how brand mentions are distributed across all AI responses for your tracked prompts. This tells you at a glance what portion of the AI conversation your brand owns.

### Top Prompts driving mentions

Lists the prompts that generate the most brand mentions. These are the topics where your brand is most likely to be referenced by AI models — and where you have the strongest presence.

### Top Brands

Shows which brands (yours and competitors) are mentioned most frequently across all tracked prompts and engines.

### Mention Rankings

A ranked table showing how your domain and competitors perform on mentions:

* Domain rank by total mentions
* Mention count and percentage share
* Change vs. previous period

### Mentions Gap

One of the most actionable sections — it identifies prompts where competitors are mentioned but your brand is not. Each gap shows:

* The prompt where you're missing
* Which competitors are mentioned instead
* The potential opportunity if you close the gap

<Tip>
  The Mentions Gap is your most direct path to improving brand visibility. Each missing mention represents a specific prompt you can target with content optimization.
</Tip>

## Response Data tab

A detailed table where each row represents a tracked prompt on a specific date:

| Column | Description |
| - | - |
| **Prompt** | The tracked prompt text |
| **LLM Engines** | Which engines were tested (shown as icons) |
| **Mentioned In** | Number of models where your brand was mentioned |
| **Total Models** | Total models tested |
| **Total Citations** | Number of citations in responses |
| **Sentiment** | Dominant sentiment (Positive, Neutral, Negative) |

### Response detail view

Click any row to open a side panel showing all AI responses for that prompt and date:

* Full response text from each engine
* Sentiment analysis per response
* Sentiment score
* Whether web search was used
* Citation count and cited URLs
* All brands mentioned in the response

Click an engine icon in the table to jump directly to that engine's response in the detail view.

## Filters

* **Date range** — select the analysis period
* **LLM engines** — filter by specific AI engines (multi-select)
* **Prompt labels** — filter all mention data to only prompts tagged with one or more labels. Labels are assigned to prompts in [Prompt Management](/analytics/prompts). The filter appears automatically once you have labeled prompts.
* **Brand Mentioned** — show all responses, only those mentioning your brand, or only those where your brand is absent
* **Website Cited** — show all responses, only those citing your site, or only those not citing your site
* **Variation** — filter by first response or any variation
* **Search text** — search within prompt text or response content

<Info>
  The "Brand NOT mentioned" filter combined with "Website NOT cited" is particularly useful for finding prompts where you have zero presence — your biggest growth opportunities.
</Info>

## Exporting data

The Response Data tab supports CSV export. Click the export button to download all filtered response data.

## Related pages

<CardGroup>
  <Card title="Understanding Responses" icon="file-lines" href="/metrics/responses">
    How responses are collected and brand detection works.
  </Card>

  <Card title="Citations Analysis" icon="link" href="/analytics/citations-page">
    Deep-dive into citation data and URL-level analysis.
  </Card>

  <Card title="Brand Sentiment" icon="face-smile" href="/analytics/brand-sentiment">
    Analyze the tone of AI responses about your brand.
  </Card>

  <Card title="Competitor Analysis" icon="users" href="/analytics/competitor-analysis">
    See how your mention share compares to competitors.
  </Card>
</CardGroup>


# Overview Dashboard
Source: https://docs.superlines.io/analytics/overview-dashboard

Your daily starting point for monitoring AI search performance across all dimensions.

The Overview Dashboard is the first page you see after signing in. It provides a high-level snapshot of your brand's AI search performance across three key dimensions: brand visibility, citation rate, and crawler traffic.

## Dashboard tabs

The Overview is organized into three tabs, each focused on a different aspect of your AI search presence.

### Brand Visibility tab

The default view showing how often your brand is mentioned in AI responses.

**KPI cards at the top:**

| Card | What it shows |
| - | - |
| **Top Prompt** | Your best-performing prompt and its brand visibility percentage |
| **Brand Visibility** | Your overall brand visibility with change vs. previous period |
| **Top Engine** | The AI engine where your brand performs best |
| **Top Label** | The prompt label category with highest visibility |

**Below the KPIs:**

* **Brand Visibility Trend** — a chart showing your visibility over time, with options to show a rolling average, competitor trends, and previous period comparison
* **Brand Leaders** — a ranked table of all brands mentioned in your tracked prompts, with mentions, change, and visibility percentage
* **Engine Performance** — visibility breakdown by AI engine
* **Prompts table** — all tracked prompts with their brand visibility, citation rate, total responses, and labels

### Citation Rate tab

Focused on how often AI responses link to your website.

**KPI cards:**

| Card | What it shows |
| - | - |
| **Top Prompt** | Your best prompt by citation rate |
| **Citation Rate** | Overall citation rate with change vs. previous period |
| **Top Fan-Out Query** | The LLM-generated query driving the most citations |

**Below the KPIs:**

* **Citation Rate Trend** — chart showing your citation rate over time
* **Top Cited URLs** — which pages on your site are cited most
* **LLM Search Queries** — fan-out queries with their associated prompt, citation rate, and search volume
* **Prompts by Citation Rate** — all prompts ranked by citation performance

### Crawler Traffic tab

Shows how AI bots are crawling your website.

If you haven't installed the tracking script yet, this tab shows setup instructions with platform-specific guides for Webflow, WordPress, Shopify, Next.js, and edge function setups (Netlify, Vercel).

Once tracking is active, you'll see:

* **Bot Traffic Trend** — total crawler traffic over time, or broken down by individual bot
* **Top Bots** — which AI crawlers visit your site most
* **Top Crawled Pages** — which pages get the most bot traffic
* **Bot Categories** — breakdown of crawler types

<Info>
  For a deeper analysis of crawler traffic with human vs. bot comparison and AI-specific bot tracking, see the dedicated [Crawler Analytics](/website/crawler-analytics) page.
</Info>

## Filters

All tabs share a common set of filters:

* **Date range** — select the time period to analyze, with an option to include the current (incomplete) day
* **Engine** — filter by specific AI engines (multi-select)
* **Prompt** — focus on a specific tracked prompt
* **Label** — filter by prompt label categories

Filter selections are shown as chips above the content, making it easy to see what's active and clear individual filters.

## Using the Overview as a daily workflow

<Tip>
  Start each day on the Overview Dashboard to quickly spot changes. Look for:

  1. **Brand Visibility changes** — any significant drops or gains in the trend
  2. **KPI shifts** — check if your top prompt, engine, or label has changed
  3. **New leaders** — scan the Brand Leaders table for competitor movement
  4. **Citation opportunities** — switch to the Citation Rate tab to find prompts with low citation rates

  Then drill into specific analytics pages for deeper investigation.
</Tip>

## Exporting data

Each table on the Overview supports CSV and JSON export. Click the export dropdown on any table to download the data.

## Next steps

<CardGroup>
  <Card title="Visibility Dashboard" icon="chart-line" href="/analytics/visibility-dashboard">
    Deep-dive into brand visibility with the full dashboard.
  </Card>

  <Card title="Key Metrics" icon="chart-bar" href="/metrics/key-metrics">
    Understand what each metric means and how it's calculated.
  </Card>

  <Card title="Competitor Analysis" icon="users" href="/analytics/competitor-analysis">
    Compare your performance against competitors.
  </Card>

  <Card title="Citations Analysis" icon="link" href="/analytics/citations-page">
    Explore citation data in detail.
  </Card>
</CardGroup>


# Visibility Dashboard
Source: https://docs.superlines.io/analytics/visibility-dashboard

The main analytics dashboard for monitoring brand visibility, citations, and AI search performance.

The Visibility Dashboard is the most comprehensive analytics page in Superlines. It provides detailed views of your brand's AI search performance across four tabs — Overview, Prompts, Fan-Out Queries, and Latest Mentions.

## Overview tab

The default view with your core analytics:

### KPI cards

| Card | What it shows |
| - | - |
| **Brand Visibility** | Radial metric showing mentions out of total responses, with trend |
| **Citation Rate** | Radial metric showing citations out of total responses, with trend |
| **Sentiment Score** | Breakdown of positive, neutral, and negative responses |
| **LLM Breakdown** | Brand visibility by individual AI engine |

### Trend charts

* **Brand Visibility Trend** — your visibility over time, with options to overlay competitor trends, toggle annotations, and compare to the previous period
* **Citation Rate Trend** — citation performance over time with annotations

### Average Position

Shows where your brand typically appears in AI responses:

* Average position metric with rank
* Position trend chart over time
* Competitor position comparison
* Position leaderboard table ranking all brands

<Info>
  Average Position measures the order in which brands are mentioned. Position 1 means your brand is mentioned first — the most prominent placement. See [Key Metrics](/metrics/key-metrics) for the full definition.
</Info>

### Industry Ranking

A "Brands by Brand Visibility" table ranking all detected brands:

* Rank, brand name, mention count, change, and visibility percentage
* Toggle **Only my competitors** to filter the view — **on by default**, showing only your configured competitors plus your own brand; switch it off to rank all brands detected in AI responses
* Export as CSV or JSON

<Info>
  The **Industry Average** shown here (and referenced on the KPI cards) is the unweighted mean across the top 10 brands currently shown in this table, and it always includes your own brand. Changing the toggle changes which brands feed the average. See [Key Metrics](/metrics/key-metrics) for details.
</Info>

### Insight cards

Four quick-insight cards highlighting notable data points:

* **Top Prompt** — your highest-performing prompt
* **Top Competitor** — the competitor closest to your visibility
* **Top Query** — the highest-volume fan-out query related to your prompts
* **Priority Query** — a fan-out query with high potential but low current performance

### Additional sections

* **Visibility Rankings by Topic** — how your brand performs across content topics
* **Latest Brand Mentions** — recent AI response snippets mentioning your brand

## Prompts tab

A detailed table of all your tracked prompts with management capabilities:

| Column | Description |
| - | - |
| **Status** | Active, Paused, or Processing |
| **Prompt** | The tracked prompt text |
| **Brand Visibility** | Visibility percentage for this prompt |
| **Citation Rate** | Citation rate for this prompt |
| **Tests** | Number of times this prompt has been tested |
| **Mentions** | Brand mention count |
| **Labels** | Assigned prompt labels |

### Filters

* **Status** — All, Active, Paused, Processing
* **Labels** — multi-select label filter
* **Done status** — All, Done, Not done
* **Search** — text search across prompts

### Actions

* Edit prompt details
* Pause or resume testing
* Delete prompts
* Add new prompts via dialog
* Navigate to [Prompt Import](/account/prompt-import) or [Content](/content/overview)

## Queries tab

Shows fan-out queries — the LLM-generated search queries that AI models produce during conversations about your tracked prompts.

| Column | Description |
| - | - |
| **Query** | The LLM-generated search query |
| **Prompt** | Which tracked prompt triggered this query |
| **Citation Rate** | How often your site is cited for this query |
| **Search Volume** | Estimated search volume of the query |

<Tip>
  Fan-out queries represent what AI models actually search for when answering user prompts. High-volume queries with low citation rates are your best content optimization targets.
</Tip>

## Mentions tab

A feed of the latest AI responses that mention your brand, showing actual response text, the AI engine, and when it was generated.

## Export Report

The dashboard includes a full report builder accessed via the **Export** button:

* Customizable sections (visibility, citations, rankings, competitors, sentiment, site health)
* White-label branding (logo, colors, company name)
* Shareable report URLs
* PDF download

See [Export & Reporting](/features/export-reporting) for full details.

## Response details

Click any prompt or mention to open a detailed view of individual AI responses:

* Full response text with formatted or original view
* Brand mention status
* Citation details and cited URLs
* All brands mentioned in the response
* Engine and model information

## Annotations

Add notes to trend charts to mark significant events (content launches, algorithm changes, competitor moves). Annotations appear as markers on the chart and are visible to your team.

## Related pages

<CardGroup>
  <Card title="Overview Dashboard" icon="gauge" href="/analytics/overview-dashboard">
    High-level KPI snapshot across all dimensions.
  </Card>

  <Card title="Key Metrics" icon="chart-bar" href="/metrics/key-metrics">
    Understand how every metric is calculated.
  </Card>

  <Card title="Prompt Management" icon="list" href="/account/prompts">
    Manage your tracked prompts.
  </Card>

  <Card title="Export & Reporting" icon="file-export" href="/features/export-reporting">
    Generate PDF reports from dashboard data.
  </Card>
</CardGroup>


# Authentication
Source: https://docs.superlines.io/api/authentication

Authenticate with the Superlines REST API to use AI Search intelligence in your own dashboards and reporting.

Use the REST API to bring granular AI Search data into the dashboards, applications and reporting workflows your organization already uses. The same intelligence is available in the Superlines platform and through the [MCP server](/mcp/overview) in tools such as Claude, ChatGPT, Cursor and n8n.

The API uses **Bearer token** authentication. Include your token in the `Authorization` header of every request.

## Supported token types

| Token type | Format | Use case |
| - | - | - |
| **API Key** | `sl_live_...` | Server-side integrations, scripts, CI/CD |
| **OAuth Access Token** | JWT | MCP clients using OAuth flow |
| **Firebase ID Token** | JWT | Internal/browser-based access |

## Getting your API key

<Steps>
  <Step title="Go to Organization Settings → API Keys" />

  <Step title="Click Generate API Key">
    A key is created with the format `sl_live_...`
  </Step>

  <Step title="Copy and store it securely">
    Keys are shown only once at creation.
  </Step>
</Steps>

## Using your API key

Include it in the `Authorization` header. The API supports both **GET** and **POST** requests.

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer sl_live_YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"endpoint": "metrics", "metrics": ["brand_visibility"]}'
```

Or in code:

<Tabs>
  <Tab title="JavaScript">
    ```javascript theme={null}
    const response = await fetch('https://api.superlines.io', {
      method: 'POST',
      headers: {
        'Authorization': 'Bearer sl_live_YOUR_API_KEY',
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        endpoint: 'metrics',
        metrics: ['brand_visibility', 'citation_rate'],
        brands: ['My Brand'],
        startDate: '2025-01-01',
        endDate: '2025-01-31'
      })
    });
    const data = await response.json();
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={null}
    import requests

    response = requests.post(
        'https://api.superlines.io',
        headers={
            'Authorization': 'Bearer sl_live_YOUR_API_KEY',
            'Content-Type': 'application/json'
        },
        json={
            'endpoint': 'metrics',
            'metrics': ['brand_visibility', 'citation_rate'],
            'brands': ['My Brand'],
            'startDate': '2025-01-01',
            'endDate': '2025-01-31'
        }
    )
    data = response.json()
    ```
  </Tab>

  <Tab title="cURL">
    ```bash theme={null}
    curl -X POST https://api.superlines.io \
      -H 'Authorization: Bearer sl_live_YOUR_API_KEY' \
      -H 'Content-Type: application/json' \
      -d '{
        "endpoint": "metrics",
        "metrics": ["brand_visibility", "citation_rate"],
        "brands": ["My Brand"],
        "startDate": "2025-01-01",
        "endDate": "2025-01-31"
      }'
    ```
  </Tab>
</Tabs>

## Error responses

| Status | Meaning |
| - | - |
| `401 Unauthorized` | Missing or invalid token |
| `403 Forbidden` | Token doesn't have permission for this resource |
| `429 Too Many Requests` | Rate limit exceeded — slow down |

## Security best practices

<Warning>
  * **Never expose API keys in client-side code** — they belong on your server
  * **Use environment variables** to store keys
  * **Rotate keys regularly** and revoke compromised ones immediately
  * **One key per integration** — makes it easy to revoke individually
</Warning>


# API Endpoints
Source: https://docs.superlines.io/api/endpoints

REST API access to granular AI Search data for dashboards, applications and reporting workflows.

These endpoints return the same granular AI Search data enterprise marketing teams and agencies use in the platform: mentions, position, share of voice, citations, sources, prompts and competitive performance across brands and markets.

**Base URL:** `https://api.superlines.io`

All requests require [authentication](/api/authentication) via Bearer token. The API supports both **GET** (query parameters) and **POST** (JSON body) requests.

Every request includes an `endpoint` parameter that selects which data to return. Your API key determines which brands (domains) are accessible — you can optionally narrow results with the `brands` parameter.

<Info>
  Your API key is scoped to your organization. If you omit the `brands` parameter, data for **all** brands in your organization is returned.
</Info>

## Common parameters

These parameters are shared across most endpoints.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `endpoint` | string | Yes | Which data to retrieve: `metrics`, `brands`, `citations`, `queries`, `prompts`, `responses` |
| `brands` | string or string\[] | No | Filter by brand name(s). Omit to include all brands in your organization. |
| `labels` | string or string\[] | No | Filter by prompt label(s). Only data for prompts with these labels is returned. |
| `startDate` | string | No | Start date (`YYYY-MM-DD`) |
| `endDate` | string | No | End date (`YYYY-MM-DD`) |

***

## Metrics

### `endpoint=metrics`

Retrieve core performance metrics for your brands.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `metrics` | string or string\[] | **Yes** | Comma-separated or array: `brand_visibility`, `citation_rate`, `citations`, `share_of_citations`, `share_of_voice`, `mentions`, `tests`, `responses` |
| `brands` | string or string\[] | No | Filter by brand name(s) |
| `labels` | string or string\[] | No | Filter by prompt label(s) |
| `startDate` | string | No | Start date (`YYYY-MM-DD`) |
| `endDate` | string | No | End date (`YYYY-MM-DD`) |
| `granularity` | string | No | `total` (default), `daily`, `weekly`, `monthly` |
| `groupBy` | string or string\[] | No | Group results by dimension(s). Valid dimensions: `llm_service`, `llm_model`, `prompt_id`, `domain_id`, `intent`, `country`, `naics_code`, `brand` |
| `filters` | object | No | Additional filter criteria (see below) |

**Filters object:**

| Key | Type | Description |
| - | - | - |
| `llmService` | string or string\[] | Filter by AI engine (e.g. `"chatgpt"`, `"perplexity"`) |
| `llmModel` | string or string\[] | Filter by specific model |
| `promptId` | string or string\[] | Filter by prompt ID(s) |
| `intent` | string | Filter by intent type |
| `country` | string | Filter by country |
| `naicsCode` | string | Filter by NAICS industry code |

**Example — aggregated metrics:**

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "metrics",
    "metrics": ["brand_visibility", "citation_rate", "share_of_voice"],
    "brands": ["My Brand"],
    "startDate": "2025-01-01",
    "endDate": "2025-01-31",
    "granularity": "weekly"
  }'
```

**Example — metrics grouped by prompt:**

Use `groupBy` with `prompt_id` to get metrics broken down per prompt instead of aggregated. To discover prompt IDs, use the [prompts endpoint](#prompts).

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "metrics",
    "metrics": ["brand_visibility", "citation_rate"],
    "brands": ["My Brand"],
    "groupBy": ["prompt_id"],
    "startDate": "2025-01-01",
    "endDate": "2025-01-31"
  }'
```

You can also combine `groupBy` with `filters` to narrow results to specific prompts:

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "metrics",
    "metrics": ["brand_visibility", "citation_rate"],
    "groupBy": ["prompt_id"],
    "filters": {
      "promptId": ["prompt_abc123", "prompt_def456"]
    }
  }'
```

**Example (GET):**

```bash theme={null}
curl -H "Authorization: Bearer $API_KEY" \
  "https://api.superlines.io?endpoint=metrics&metrics=brand_visibility,citation_rate&brands=My+Brand&startDate=2025-01-01&endDate=2025-01-31&granularity=weekly"
```

**Response:**

```json theme={null}
{
  "success": true,
  "endpoint": "metrics",
  "data": [ ... ],
  "metadata": {
    "metrics": ["brand_visibility", "citation_rate", "share_of_voice"],
    "brands": ["My Brand"],
    "dateRange": {
      "startDate": "2025-01-01",
      "endDate": "2025-01-31"
    },
    "granularity": "weekly",
    "totalRows": 4,
    "executionTime": "320ms"
  }
}
```

<Tip>
  **Per-prompt workflow:** Call `endpoint=prompts` to get prompt IDs, then use `endpoint=metrics` with `groupBy: ["prompt_id"]` to get `brand_visibility`, `citation_rate`, and other metrics for each individual prompt.
</Tip>

***

## Brand mentions

### `endpoint=brands`

Get brand mention data with sentiment and share of voice.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string or string\[] | No | Filter by brand name(s) |
| `labels` | string or string\[] | No | Filter by prompt label(s) |
| `startDate` | string | No | Start date (`YYYY-MM-DD`) |
| `endDate` | string | No | End date (`YYYY-MM-DD`) |
| `granularity` | string | No | `total` (default), `daily`, `weekly`, `monthly` |
| `groupBy` | string or string\[] | No | Group results by dimension(s). Valid dimensions: `llm_service`, `llm_model`, `prompt_id`, `domain_id`, `intent`, `country`, `naics_code` |
| `filters` | object | No | Additional filter criteria (see below) |
| `topN` | number | No | Max brands to return (default: 50) |
| `includeSentiment` | boolean | No | Include sentiment data (default: true) |
| `includeShareOfVoice` | boolean | No | Include SOV data (default: true) |
| `minMentions` | number | No | Minimum mention count to include (default: 1) |

**Filters object:**

| Key | Type | Description |
| - | - | - |
| `llmService` | string or string\[] | Filter by AI engine |
| `llmModel` | string or string\[] | Filter by specific model |
| `promptId` | string or string\[] | Filter by prompt ID(s) |
| `intent` | string | Filter by intent type |
| `country` | string | Filter by country |

**Example:**

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "brands",
    "brands": ["My Brand"],
    "startDate": "2025-01-01",
    "endDate": "2025-01-31",
    "groupBy": ["brand"],
    "includeSentiment": true
  }'
```

***

## Citations

### `endpoint=citations`

Get citation data at domain or URL level.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string or string\[] | No | Filter by brand name(s) |
| `labels` | string or string\[] | No | Filter by prompt label(s) |
| `startDate` | string | No | Start date (`YYYY-MM-DD`) |
| `endDate` | string | No | End date (`YYYY-MM-DD`) |
| `granularity` | string | No | `total` (default), `daily`, `weekly`, `monthly` |
| `groupBy` | string or string\[] | No | Group results by dimension(s). Valid dimensions: `llm_service`, `llm_model`, `prompt_id`, `domain_id`, `country`, `topic`, `language` |
| `filters` | object | No | Additional filter criteria (see below) |
| `topN` | number | No | Max results (default: 100) |
| `aggregateBy` | string | No | `domain` (default) or `url` |
| `includeUrls` | boolean | No | Include URL-level data (default: true) |

**Filters object:**

| Key | Type | Description |
| - | - | - |
| `llmService` | string or string\[] | Filter by AI engine |
| `llmModel` | string or string\[] | Filter by specific model |
| `promptId` | string or string\[] | Filter by prompt ID(s) |
| `domain` | string | Filter by cited domain (partial match) |
| `country` | string | Filter by country |
| `topic` | string | Filter by topic |

**Example:**

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "citations",
    "brands": ["My Brand"],
    "aggregateBy": "url",
    "startDate": "2025-01-01",
    "endDate": "2025-01-31",
    "topN": 50
  }'
```

***

## Queries

### `endpoint=queries`

Get search query data associated with your tracked prompts.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string or string\[] | No | Filter by brand name(s) |
| `promptId` | string | No | Filter by a specific prompt ID |
| `groupBy` | string or string\[] | No | Group results by dimension(s). Valid dimensions: `llm_service`, `llm_model`, `prompt_id`, `domain_id`, `country`, `naics_code`, `standardized_topic`, `business_type` |
| `filters` | object | No | Additional filter criteria (see below) |
| `topN` | number | No | Max results (default: 100) |
| `sortBy` | string | No | `prompts` (default) or `query` |
| `minVolume` | number | No | Minimum volume threshold (default: 0) |
| `includeMetadata` | boolean | No | Include metadata (default: true) |

**Filters object:**

| Key | Type | Description |
| - | - | - |
| `llmService` | string or string\[] | Filter by AI engine |
| `intent` | string | Filter by intent type |
| `userJourneyStage` | string | Filter by user journey stage |
| `category` | string | Filter by category |
| `country` | string | Filter by country |
| `naicsCode` | string | Filter by NAICS industry code |

**Response fields:**

| Field | Type | Description |
| - | - | - |
| `query` | string | The search query text |
| `prompt_count` | number | Number of prompts associated with this query |
| `last_updated` | string | Timestamp of the most recent data |
| `brands_mentioned` | number | Total number of distinct brands detected in the AI responses for this query — includes **all** brands (yours, competitors, and others), not only your own |

<Info>
  `brands_mentioned` counts every distinct brand, product, or service name found in the AI-generated response text. For example, if a response mentions your brand plus three competitors, `brands_mentioned` is 4.
</Info>

**Example:**

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "queries",
    "brands": ["My Brand"],
    "sortBy": "prompts",
    "topN": 50
  }'
```

***

## Prompts

### `endpoint=prompts`

List tracked prompts for your brands.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string or string\[] | No | Filter by brand name(s) |
| `status` | string or string\[] | No | Filter by status: `active`, `paused`, etc. |
| `intent` | string or string\[] | No | Filter by intent type |
| `labels` | string or string\[] | No | Filter by label(s) |
| `sortBy` | string | No | `createdAt` (default), `lastRunAt`, `promptText` |
| `topN` | number | No | Max results (default: 100) |
| `includeMetadata` | boolean | No | Include extended metadata fields (default: true) |

**Response fields:**

| Field | Type | Description |
| - | - | - |
| `promptId` | string | Unique identifier for the prompt. Use this with `groupBy` or `filters.promptId` on other endpoints. |
| `promptText` | string | The prompt text |
| `status` | string | Current status (`active`, `paused`, etc.) |
| `intent` | string | Detected intent type |
| `category` | string | Prompt category |
| `labels` | string\[] | Labels assigned to this prompt |
| `createdAt` | string | ISO 8601 creation timestamp |
| `lastRunAt` | string | ISO 8601 timestamp of the last analysis run |
| `domainId` | string | The brand (domain) this prompt belongs to |

When `includeMetadata` is `true`, additional fields are returned: `userJourneyStage`, `commercial`, `source`, `naicsCode`, `country`.

**Example:**

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "prompts",
    "brands": ["My Brand"],
    "status": "active",
    "topN": 50
  }'
```

**Response:**

```json theme={null}
{
  "success": true,
  "endpoint": "prompts",
  "data": [
    {
      "promptId": "abc123",
      "promptText": "What is the best project management tool?",
      "status": "active",
      "intent": "informational",
      "category": "software",
      "labels": ["product", "comparison"],
      "createdAt": "2025-01-15T10:30:00.000Z",
      "lastRunAt": "2025-01-31T08:00:00.000Z",
      "domainId": "domain_xyz"
    }
  ],
  "metadata": { ... }
}
```

<Tip>
  Use `promptId` from this endpoint to filter or group data on the `metrics`, `brands`, `citations`, and `queries` endpoints — for example, `groupBy: ["prompt_id"]` or `filters: { "promptId": ["abc123"] }`.
</Tip>

***

## Responses

### `endpoint=responses`

Get raw AI response data for your tracked prompts.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string or string\[] | No | Filter by brand name(s) |
| `labels` | string or string\[] | No | Filter by prompt label(s) |
| `startDate` | string | No | Start date (`YYYY-MM-DD`) |
| `endDate` | string | No | End date (`YYYY-MM-DD`) |
| `filters` | object | No | Additional filter criteria (see below) |
| `sortBy` | string | No | `createdAt` (default) or `promptId` |
| `topN` | number | No | Max results (default: 100) |
| `includeFullText` | boolean | No | Include full response text (default: false) |

**Filters object:**

| Key | Type | Description |
| - | - | - |
| `llmService` | string or string\[] | Filter by AI engine |
| `llmModel` | string or string\[] | Filter by specific model |
| `promptId` | string or string\[] | Filter by prompt ID(s) |
| `brandMentioned` | boolean | Only responses where your brand is/isn't mentioned |
| `websiteCited` | boolean | Only responses where your website is/isn't cited |
| `intent` | string | Filter by intent type |
| `country` | string | Filter by country |

**Response fields:**

| Field | Type | Description |
| - | - | - |
| `promptId` | string | The prompt this response belongs to |
| `promptText` | string | The prompt text |
| `answerText` | string | The AI-generated response text (when `includeFullText` is true) |
| `llmService` | string | AI engine that generated the response (e.g. `"chatgpt"`, `"perplexity"`) |
| `llmModel` | string | Specific model used |
| `brandMentioned` | boolean | Whether your brand was mentioned in this response |
| `websiteCited` | boolean | Whether your website was cited in this response |
| `brands` | string\[] | All brands detected in the response text (yours, competitors, and others) |
| `citations` | object\[] | Citations found in the response, each with `url` and `cited` fields |

**Example:**

```bash theme={null}
curl -X POST https://api.superlines.io \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "responses",
    "brands": ["My Brand"],
    "startDate": "2025-01-01",
    "endDate": "2025-01-31",
    "includeFullText": true,
    "topN": 20
  }'
```

***

## Rate limits

| Plan | Requests/minute |
| - | - |
| Starter | 60 |
| Growth | 120 |
| Enterprise | 300 |

Exceeding the rate limit returns a `429` status code with `X-RateLimit-Remaining` and `X-RateLimit-Reset` headers. Implement exponential backoff in your integration.

## Response format

All successful responses follow this structure:

```json theme={null}
{
  "success": true,
  "endpoint": "metrics",
  "data": [ ... ],
  "metadata": {
    "brands": ["My Brand"],
    "dateRange": { "startDate": "2025-01-01", "endDate": "2025-01-31" },
    "totalRows": 42,
    "executionTime": "215ms"
  }
}
```

Error responses:

```json theme={null}
{
  "error": "Invalid endpoint parameter",
  "message": "Endpoint must be one of: metrics, brands, citations, queries, prompts, responses",
  "validEndpoints": ["metrics", "brands", "citations", "queries", "prompts", "responses"]
}
```


# Content Module
Source: https://docs.superlines.io/content/overview

Create and optimize content designed to perform well in AI-powered search.

The Content module helps you create new content optimized for Answer Engine Optimization (AEO) and enhance existing content to improve its AI search visibility. It connects your analytics insights directly to content output — so you can act on what you learn.

## Content Generation

Create high-performing content using AI-powered templates designed specifically for LLM platform performance.

### How to create content

<Steps>
  <Step title="Open the Content page">
    Navigate to **Content** in the sidebar. You'll land on the **Generate Content** tab.
  </Step>

  <Step title="Click Create New Content">
    Click the **Create New Content** card. This opens the template selection modal.
  </Step>

  <Step title="Choose a template">
    Browse templates by category — Informational, Comparative, Social, or Transactional. Each template is designed for a specific content type and AI search intent. You can search templates or mark favorites for quick access.
  </Step>

  <Step title="Configure and generate">
    Fill in the template parameters (topic, keywords, audience, etc.) and generate your content.
  </Step>

  <Step title="Review and edit">
    Review the generated content, make edits, and export when ready.
  </Step>
</Steps>

### Template categories

| Category | Best for | Examples |
| - | - | - |
| **Informational** | Educational content that answers questions | How-to guides, explainers, FAQs |
| **Comparative** | Content that compares options | Product comparisons, alternatives lists |
| **Social** | Community and discussion content | Social posts, discussion starters |
| **Transactional** | Content that drives action | Landing pages, product descriptions |

### Managing articles

The articles table shows all your generated content with:

* **Title** — the article name
* **Status** — Draft, Published, Completed, In Progress, or Generating
* **Template** — which template was used
* **Word Count** — article length
* **Last Updated** — when it was last modified

You can search articles by title, filter by status or template, and perform actions on each article: view/edit, duplicate, export as Markdown, or delete.

## Content Optimization (Beta)

Enhance existing content to boost its AI visibility. Upload or import content and receive AI-powered suggestions to improve how AI models interpret and cite your pages.

To optimize existing content, click **Optimize Existing Content** on the Generate Content tab, which takes you to the content import flow.

<Note>
  Content Optimization is currently in Beta. Features and capabilities may evolve based on feedback.
</Note>

## Content Tools

The **Tools** tab provides three standalone utilities for quick content optimization tasks:

### URL Slug Optimizer

Analyze and improve your URL slugs for AI search. Enter a domain and slug to get analysis on:

* Keyword presence
* Readability score
* Length optimization
* Stop word usage

### Meta Description Generator

Generate AI-optimized meta descriptions. Enter your page title and target keyword to receive a description with analysis of length and keyword placement.

### Headline Analyzer

Score your headlines for effectiveness. Enter a headline to see:

* Overall score
* Word and character count analysis
* Power word usage
* Number presence
* Question format effectiveness

## How content connects to analytics

Content creation in Superlines is informed by your analytics data:

* **Prompt data** reveals what questions your audience asks AI — create content that answers them
* **Citation analysis** shows which URLs get cited — optimize your content to earn more citations
* **Competitor insights** highlight content gaps — create content where competitors are winning
* **Brand sentiment** indicates how AI perceives your brand — shape your messaging accordingly

<CardGroup>
  <Card title="AI Search Checker" icon="magnifying-glass" href="/tools/ai-search-checker">
    Check if your content is optimized for AI search.
  </Card>

  <Card title="Schema Optimiser" icon="code" href="/tools/schema-optimiser">
    Add structured data to help AI understand your content.
  </Card>
</CardGroup>


# Frequently Asked Questions
Source: https://docs.superlines.io/faq

Answers to the most common questions about Superlines, an AI Search Intelligence platform for enterprise marketing teams and marketing agencies.

## About Superlines

<AccordionGroup>
  <Accordion title="What is Superlines?">
    Superlines is an AI Search Intelligence platform for enterprise marketing teams and marketing agencies. It provides granular visibility into brand mentions, position, share of voice, citations, sources and competitive performance across AI engines, brands and markets.

    It is broader than a traditional SEO rank tracker. The purpose is to show how brands are represented, compared, cited and recommended across AI Search.
  </Accordion>

  <Accordion title="Who is Superlines for?">
    Superlines is built primarily for two groups:

    * **Enterprise marketing teams** that measure and improve AI Search visibility across brands, products and markets. Brand, SEO, content, communications, digital, growth and analytics teams use it inside that organization.
    * **Marketing agencies** that manage AI Search visibility across client portfolios, brands, markets and AI engines.

    It is particularly suited to multi-market and multi-brand organizations: many countries and languages, large prompt sets, many competitors, and centralized reporting.

    Self-service plans are available for smaller teams that need the same kind of intelligence at lower data volumes.
  </Accordion>

  <Accordion title="How can we use Superlines data outside the platform?">
    Use Superlines where your team already works.

    * **MCP** connects Superlines to AI assistants and workflows such as Claude, ChatGPT, Cursor and n8n. See the [setup guide](/mcp/setup).
    * **API** brings granular AI Search data into your own dashboards, applications and reporting workflows. See [authentication](/api/authentication).
    * **Looker Studio** is available for custom dashboards and scheduled reports. See the [Looker Studio guide](/integrations/looker-studio).
  </Accordion>
</AccordionGroup>

## Reporting & data export

<AccordionGroup>
  <Accordion title="How can I export the most comprehensive report?">
    There are several ways to get reports out of Superlines:

    **Quick options:**

    * Take a screenshot of the **Visibility** page at [analytics.superlines.io/dashboard](https://analytics.superlines.io/dashboard) or the **Overview** page
    * Use the **Export** button in the top-right corner of the Visibility page for a clean one-page summary

    **Advanced options:**

    * **REST API** — pull data into your own reporting system via the [Metrics API](/api/endpoints) and visualize it however you like
    * **MCP Server** — use Claude, ChatGPT, Cursor or n8n to ask questions about your data and generate reports ([setup guide](/mcp/setup))
    * **Looker Studio** — connect to Looker Studio for custom dashboards and scheduled reports ([integration guide](/integrations/looker-studio))
  </Accordion>

  <Accordion title="How are prompts defined during auditing?">
    When you create a brand, Superlines **automatically discovers relevant prompts** based on your industry, target audience, and competitors. These auto-generated prompts identify "hot" conversation topics that are relevant to your business — providing a great starting point for a lightweight AI search audit.

    You can then refine the list by adding your own high-priority questions, removing irrelevant ones, and organizing them with labels.
  </Accordion>
</AccordionGroup>

## Prompts & data

<AccordionGroup>
  <Accordion title="How do you know which prompts are relevant?">
    Superlines uses a multi-layered approach:

    1. **Industry analysis** — based on your business category and target audience
    2. **Competitor monitoring** — what topics competitors are being mentioned for
    3. **AI search trends** — tracking emerging queries in your space
    4. **Search data correlation** — mapping AI prompts to traditional search queries
    5. **User feedback** — learning from prompts you add, pause, or ignore

    The system continuously refines its suggestions as it learns what matters for your brand.
  </Accordion>

  <Accordion title="How do you know what people ask AI?">
    Our data comes from multiple sources:

    * **AI query databases** — aggregated, anonymized query data from AI platforms
    * **Search behavior analysis** — correlating traditional search queries with AI-era prompts
    * **Industry trend detection** — monitoring trending topics by vertical
    * **Platform observation** — analyzing public AI interactions and community forums

    <Note>
      We don't access individual user queries or private conversations. All data is aggregated and anonymized.
    </Note>
  </Accordion>

  <Accordion title="Can I buy more prompts or add more LLM engines later?">
    **Yes, absolutely.** You can upgrade your plan at any time to get:

    * More tracked prompts
    * Additional AI engines
    * More brands/domains
    * More team seats
    * API and MCP access

    Changes take effect immediately. Contact your account manager or upgrade directly in Organization Settings.
  </Accordion>

  <Accordion title="Why don't you have historical data from before I signed up?">
    Superlines begins collecting data **from the moment you create a brand**. We can't provide historical data because:

    1. **Real-time testing** — we send your prompts to AI engines and capture actual responses, which requires active monitoring
    2. **AI responses change constantly** — unlike traditional search rankings, AI responses are dynamic and non-deterministic
    3. **No historical API** — AI platforms don't provide historical response data

    Your historical data **begins building from day one** and becomes more valuable over time as trends emerge.
  </Accordion>

  <Accordion title="Why do numbers change rapidly in the beginning?">
    This is normal behavior during the first 24–48 hours:

    * **Small sample sizes** make percentages volatile — one new response has an outsized impact
    * **Initial responses** are being collected across all engines simultaneously
    * Numbers **stabilize** within 48 hours as more responses are collected

    Wait at least 48 hours before drawing conclusions from your data.
  </Accordion>
</AccordionGroup>

## Metrics & definitions

<AccordionGroup>
  <Accordion title="What's the difference between Brand Visibility and Share of Voice?">
    These measure two different things:

    **Brand Visibility** = *"How often is my brand mentioned?"*

    ```
    Your brand mentions / Total AI responses × 100
    ```

    **Share of Voice** = *"How do I compare to competitors?"*

    ```
    Your brand mentions / All brand mentions (yours + competitors) × 100
    ```

    **Example:** If 100 prompts are tested:

    * Your brand appears in 30 responses → **Brand Visibility = 30%**
    * Total brand mentions across all responses: 200 (yours: 50, competitors: 150) → **SOV = 25%**

    Brand Visibility tells you about **your absolute presence**. Share of Voice tells you about **your competitive position**.
  </Accordion>

  <Accordion title="What is Average Position and how is it calculated?">
    **Average Position** measures where your brand typically appears in the order of brand mentions within AI-generated answers. Position 1 = mentioned first, Position 2 = mentioned second, etc.

    The average is **volume-weighted** — days with more AI responses have proportionally more influence on the score. This is similar to how Google Search Console calculates impression-weighted position.

    **Key points:**

    * Only responses where your brand is **actually mentioned** are included
    * Lower position = better (being mentioned first means AI models see you as a primary recommendation)
    * The **Average Position Ranking** leaderboard shows how you compare to all other brands

    **Example:** If an AI responds to "Best project management tools" and mentions Asana first, Monday.com second, and your brand third — your position for that response is 3.

    Average Position helps you understand the **quality** of your visibility. A brand can have high Brand Visibility (mentioned often) but poor Average Position (mentioned last). Improving your position means AI models treat your brand as a top recommendation.
  </Accordion>

  <Accordion title="Why is my label-level Brand Visibility higher than every prompt under it?">
    This looks impossible at first — for example, a label showing 10.1% while its five prompts show 3.5%, 6.0%, 4.2%, 0.0%, and 0.7% — but it is expected. The two levels use slightly different detection rules:

    * **Label/theme level (and the dashboard KPI card)** counts a response when your brand is found anywhere in the response text, **including common name variants** — "Yale" counts for the brand "Yale Home".
    * **Per-prompt Brand Visibility** (Prompts table and CSV export) is stricter: a response counts only when your exact brand name appears in the structured list of brands extracted from that response.

    Since the per-prompt rule is stricter, its percentages are always **lower than or equal to** what the tolerant rule would give — so every prompt can sit below the label figure.

    **To reconcile the numbers:** in the Tracked Prompts CSV export, divide **Brand Mentions** by **AI Responses** for each prompt. Those columns use the same detection as the label level, and summed across the label's prompts they reproduce the label figure exactly (e.g., 73 / 726 = 10.1%).

    Also note that the label figure is **response-weighted**, not a simple average of the prompt percentages — prompts with more responses influence it more.
  </Accordion>

  <Accordion title="What does the Industry Average include?">
    The Industry Average shown on the KPI cards and in the Industry Ranking section is the **simple (unweighted) mean** of the metric across the brands currently shown in the Industry Ranking table — at most the top 10.

    Which brands are included depends on the **Only my competitors** toggle in the Industry Ranking section:

    * **Toggle on (default):** only your configured competitors, **plus your own brand**
    * **Toggle off:** all brands detected in AI responses

    Two things to keep in mind when using it as a benchmark:

    1. Your own brand is always part of the average, so it is not a pure "everyone else" comparison.
    2. Every brand counts equally in the average, regardless of how many mentions it has.
  </Accordion>
</AccordionGroup>

## Account & billing

<AccordionGroup>
  <Accordion title="How many brands can I create?">
    This depends on your plan:

    | Plan | Brands |
    | - | - |
    | Free | 1 |
    | Starter | Multiple |
    | Growth | Multiple |
    | Enterprise | Unlimited |

    Each brand is independent with its own prompts, competitors, and analytics. Prompts are shared across all brands from your organization quota.
  </Accordion>

  <Accordion title="Do I need a cookie banner for the AI Bot Analytics?">
    **No.** Superlines does not use cookies or track website visitors. The platform works by:

    1. Sending your tracked prompts to AI engines
    2. Capturing and analyzing the responses
    3. Extracting brand mentions and citations

    This happens entirely **server-side** and doesn't involve your website visitors at all. No cookies, no tracking pixels, no GDPR consent required for the AI analytics functionality.
  </Accordion>
</AccordionGroup>

## Still have questions?

<CardGroup>
  <Card title="Contact support" icon="envelope" href="mailto:support@superlines.io">
    Email us at [support@superlines.io](mailto:support@superlines.io)
  </Card>

  <Card title="Book a demo" icon="calendar" href="https://superlines.io">
    See the platform in action with a guided walkthrough.
  </Card>
</CardGroup>


# Action Center
Source: https://docs.superlines.io/features/action-center

Track and manage AI-generated recommendations to improve your brand's AI visibility.

The Action Center is where AI-generated insights become actionable tasks. It collects recommendations from the AI Agent's analysis and organizes them into categories you can work through, complete, or dismiss.

## How actions are generated

Actions are created when the [AI Agent](/features/agent) analyzes your data and identifies opportunities for improvement. The Agent examines your brand visibility, citations, content, and technical SEO to produce specific, actionable recommendations.

Each action includes:

* A **description** of what to do
* A **category** indicating the type of work
* A **priority** level (high, medium, or low)
* A **relevant URL** linking to the tool or page where you can take action

## Action categories

| Category | What it covers |
| - | - |
| **New Content** | Topics and pages you should create to improve AI visibility |
| **Content Optimization** | Existing content that needs improvement for better AI performance |
| **Technical Optimization** | Technical SEO issues affecting your AI search presence |
| **Schema Optimization** | Structured data improvements to help AI understand your pages |
| **Community Discussion** | Opportunities on platforms like Reddit, Quora, or forums |
| **Media Outreach** | PR and media opportunities to boost brand authority |

## Managing actions

### Status workflow

Each action has one of three statuses:

| Status | Meaning |
| - | - |
| **Pending** | Not yet addressed — waiting for your attention |
| **Completed** | You've taken action and marked it as done |
| **Dismissed** | You've reviewed it and decided not to act on it |

### Working with actions

<Steps>
  <Step title="Review pending actions">
    Open the Action Center from the sidebar. Pending actions are shown by default, with a count badge indicating how many await your attention.
  </Step>

  <Step title="Filter by category">
    Use the category buttons to focus on a specific type of action. Each category shows a count of pending items.
  </Step>

  <Step title="Take action">
    Click on an action to navigate to the relevant tool or page. For example, a Schema Optimization action might link to the [Schema Optimiser](/tools/schema-optimiser).
  </Step>

  <Step title="Update the status">
    Mark the action as **Completed** when you've addressed it, or **Dismissed** if it's not relevant. You can always filter to see completed or dismissed actions later.
  </Step>
</Steps>

## Filtering and navigation

* **Status filters** — toggle between All, Pending, Completed, and Dismissed
* **Category filters** — focus on specific action types (each shows its count)
* **Refresh** — reload the action list to see newly generated recommendations

<Tip>
  Use the Action Center as a weekly workflow: review new pending actions, prioritize by category, and work through them systematically. This creates a consistent optimization loop between your analytics insights and concrete improvements.
</Tip>

## Related features

<CardGroup>
  <Card title="AI Agent" icon="robot" href="/features/agent">
    The Agent generates the recommendations you see here.
  </Card>

  <Card title="AI Search Checker" icon="magnifying-glass" href="/tools/ai-search-checker">
    Run page-level audits suggested by actions.
  </Card>

  <Card title="Content Module" icon="file-lines" href="/content/overview">
    Create content recommended by action items.
  </Card>

  <Card title="Schema Optimiser" icon="code" href="/tools/schema-optimiser">
    Implement schema changes from optimization actions.
  </Card>
</CardGroup>


# AI Agent
Source: https://docs.superlines.io/features/agent

Use the conversational AI assistant to query your data, run audits, and generate action plans.

The AI Agent is a conversational assistant built into Superlines that lets you ask natural-language questions about your brand's AI search performance, run technical audits, analyze competitors, and generate strategic action plans — all from a single chat interface.

## What the Agent can do

The Agent has access to your brand data and a suite of built-in tools:

| Capability | Examples |
| - | - |
| **Query your data** | "What's my brand visibility trend this month?", "Which prompts have the highest citation rate?" |
| **Competitor intelligence** | "Who's getting cited more than me?", "Analyze competitor X's homepage" |
| **Run audits** | "Audit my homepage for AI search readiness", "Check my schema markup" |
| **Generate action plans** | "Create a strategic action plan to improve my visibility", "What should I prioritize?" |
| **Content guidance** | "What topics should I write about?", "How can I improve my FAQ page for AI?" |

When you open the Agent, you'll see pre-built suggestion cards to help you get started:

* **"Who's getting cited?"** — Discover which domains AI models cite most for your tracked prompts
* **"Strategic action plan"** — Get a prioritized list of actions to improve your AI visibility
* **"Audit my homepage"** — Run a full AI search readiness analysis on your website
* **"Analyze a competitor"** — Deep-dive into a competitor's AI search presence

## How to use the Agent

<Steps>
  <Step title="Open the Agent">
    Navigate to **Agent** in the sidebar under AI Visibility.
  </Step>

  <Step title="Ask a question or pick a suggestion">
    Type your question in the chat input, or click one of the suggestion cards to get started.
  </Step>

  <Step title="Review the response">
    The Agent will query your data, run any necessary analyses, and return a detailed response with insights and recommendations.
  </Step>

  <Step title="Follow up">
    Ask follow-up questions to drill deeper. The Agent maintains context within the conversation.
  </Step>
</Steps>

## Example prompts

Here are effective prompts to try:

* *"Show me my brand visibility trends for the last 30 days across all engines"*
* *"Which of my tracked prompts have the lowest citation rate?"*
* *"Audit [https://example.com](https://example.com) for AI search readiness"*
* *"Compare my brand sentiment to my top 3 competitors"*
* *"What schema markup should I add to my product pages?"*
* *"Generate a weekly action plan based on my current metrics"*

## How it connects to your data

The Agent works with the same data you see across the platform:

* **Tracked prompts** — queries your prompt portfolio and performance metrics
* **Responses & mentions** — accesses AI response data across all monitored engines
* **Citations** — analyzes which URLs are being cited and where
* **Competitors** — compares your performance against configured competitors
* **Site health** — can trigger and review technical audits of your pages

<Info>
  The Agent uses the same tool set as the [MCP Server](/mcp/overview). If you're already familiar with MCP tools, the Agent provides the same capabilities through a visual chat interface.
</Info>

## Limitations

* The Agent works with data available in your Superlines account — it cannot access external data beyond what the built-in tools provide
* Audit and analysis tools require a URL input and are subject to your plan's usage limits
* Complex multi-step analyses may take a few moments to complete

## Actions from the Agent

When the Agent generates recommendations, these can appear as actionable items in the [Action Center](/features/action-center). This creates a workflow where insights from the Agent become trackable tasks.

<CardGroup>
  <Card title="Action Center" icon="list-check" href="/features/action-center">
    Track and manage recommendations from the Agent.
  </Card>

  <Card title="MCP Server" icon="plug" href="/mcp/overview">
    Use the same tools programmatically via MCP.
  </Card>
</CardGroup>


# Export & Reporting
Source: https://docs.superlines.io/features/export-reporting

Export data and generate reports from across the Superlines platform.

Superlines provides multiple ways to export data and generate reports — from quick CSV downloads on individual tables to comprehensive branded PDF reports covering all your analytics.

## Export formats

| Format | Where available | Best for |
| - | - | - |
| **CSV** | Most data tables | Importing into spreadsheets, custom analysis |
| **JSON** | Most data tables | Programmatic use, API workflows |
| **PDF** | Dashboard Export Report | Stakeholder reports, executive summaries |
| **PNG** | Charts | Presentations, social media |
| **Markdown** | AI Search Checker, Articles | Documentation, sharing analysis |

## Table exports

Most data tables across the platform have an export dropdown. Click the export icon on any table to download data as CSV or JSON. Available on:

* Overview Dashboard tables (Brand Leaders, Prompts, Top Cited URLs, LLM Queries)
* Competitor Analysis (Brand Performance table)
* Mentions / Response Data
* Citations Data
* Crawler Analytics (all tables)
* Keyword Research (keyword tables)
* Cross-Brand Analytics (Domain Performance table)

## Dashboard Export Report

The Visibility Dashboard includes a full report builder that generates comprehensive PDF reports.

### Creating a report

<Steps>
  <Step title="Open the Export modal">
    On the Visibility Dashboard, click the **Export** button in the page header.
  </Step>

  <Step title="Choose report sections">
    Select which sections to include in your report:

    * Brand visibility metrics and trends
    * Citation rates
    * Industry rankings
    * Platform performance by AI engine
    * Competitor share of voice
    * Query performance
    * Sentiment analysis
    * Site health (optional)
  </Step>

  <Step title="Configure branding">
    Customize the report with white-label branding:

    * Upload your logo
    * Set brand colors
    * Add your company name
  </Step>

  <Step title="Generate the report">
    Click generate to create your PDF report. Once ready, you can download it or share it via a unique URL.
  </Step>
</Steps>

<Tip>
  Use the shareable report URL for client reporting — it provides a professional, branded view of performance data without requiring platform access.
</Tip>

## API-based exports

For programmatic access, several dedicated export endpoints are available:

| Endpoint | Data |
| - | - |
| **Tracked Prompts** | All tracked prompts with metrics (CSV, JSON, Excel) |
| **Citations** | Citation records (CSV, JSON) |
| **Prompt Responses** | Full AI response data (CSV, JSON) |
| **Conversation Opportunities** | Fan-out query opportunities (CSV, JSON, Excel) |

See the [API Endpoints](/api/endpoints) documentation for authentication and endpoint details.

### Tracked Prompts export: metric columns explained

Three columns in the Tracked Prompts export are easy to mix up:

| Column | What it contains |
| - | - |
| **Brand Mentions** | Number of responses where your brand was detected anywhere in the response text, including common name variants. Same detection as the dashboard KPI card. |
| **Brand Visibility** | Percentage of responses where your **exact brand name** appears in the structured list of brands extracted from the response. A stricter rule, so this is typically lower than `Brand Mentions ÷ AI Responses`. |
| **Share of Voice** | Your brand's mentions as a share of **all** brand mentions (yours + competitors) in the same responses. A share-of-mentions metric, not a response rate. |

<Note>
  To reconcile the export with the dashboard's "X of Y AI responses mention your brand" figure, use **Brand Mentions ÷ AI Responses** per prompt — summed across prompts, these reproduce the dashboard and label-level Brand Visibility exactly. See the [Metrics Glossary](/analytics/metrics-glossary) for why the Brand Visibility column can differ.
</Note>

## Other export options

### Chart exports

Charts on the Competitor Analysis page can be exported as PNG images — useful for presentations and reports.

### AI Search Checker exports

Analysis results from the [AI Search Checker](/tools/ai-search-checker) can be exported as:

* **Markdown** — full analysis report
* **PDF** — formatted report
* **JSON-LD** — optimized schema markup (copy to clipboard)

### Article exports

Generated articles from the [Content Module](/content/overview) can be exported as Markdown files.

## Integrations for automated reporting

For scheduled and automated reporting, consider:

* **[Looker Studio](/integrations/looker-studio)** — connect Superlines data to Looker Studio for custom dashboards and scheduled email reports
* **[MCP Server](/mcp/overview)** — build custom reporting workflows using the MCP tools
* **[REST API](/api/endpoints)** — pull data programmatically into your own reporting systems

<CardGroup>
  <Card title="API Endpoints" icon="code" href="/api/endpoints">
    Programmatic data access for custom reporting.
  </Card>

  <Card title="Looker Studio" icon="chart-line" href="/integrations/looker-studio">
    Build custom dashboards with scheduled delivery.
  </Card>

  <Card title="MCP Server" icon="plug" href="/mcp/overview">
    Automate reporting with MCP tools.
  </Card>

  <Card title="Slack" icon="hashtag" href="/integrations/slack">
    Get automated notifications delivered to Slack.
  </Card>
</CardGroup>


# Core Concepts
Source: https://docs.superlines.io/getting-started/concepts

Understand the key concepts behind the Superlines platform.

Before diving into the platform, it helps to understand the key concepts that Superlines is built around.

## Account hierarchy

Superlines uses a hierarchical structure:

```
Organization
├── Brand (Domain) 1
│   ├── Prompts + Labels
│   ├── Competitors
│   └── Analytics
├── Brand (Domain) 2
│   ├── Prompts + Labels
│   ├── Competitors
│   └── Analytics
└── Team Members (org-wide)
```

* **Organization** — your top-level account. It holds your subscription, team members, API keys, and LLM engine configuration.
* **Brand (Domain)** — a business entity you want to monitor. Each brand has its own name, website, target market, competitors, prompts, and analytics.
* **Prompts** — the questions people ask AI. Superlines tracks how each prompt performs across AI engines.
* **Labels** — tags for organizing prompts by topic, campaign, priority, or any custom category.

## What are prompts?

A **prompt** is a question or query that people type into AI assistants. Examples:

* *"What are the best project management tools for remote teams?"*
* *"How do I improve my website's AI search visibility?"*
* *"Which CRM is best for small businesses?"*

Superlines **tracks** these prompts by testing them across multiple AI engines (ChatGPT, Copilot, etc.) and recording:

* Whether your brand is mentioned in the response
* Whether your website is cited as a source
* Which competitors appear
* The overall sentiment

## What are responses?

A **response** is an AI engine's answer to a tracked prompt. Each time Superlines tests a prompt, it captures the full response and analyzes it for:

* **Brand mentions** — does the response name your brand?
* **Brand position** — in what order is your brand mentioned relative to others?
* **Website citations** — does the response link to or reference your domain?
* **Competitor mentions** — which other brands appear?
* **Sentiment** — is the mention positive, neutral, or negative?

## What are citations?

A **citation** is a reference to a specific URL in an AI response. When an AI model links to your website (or a competitor's), that's a citation. Citations are a strong signal of content authority.

<Note>
  Not all AI engines provide citations. Models with web access (like Perplexity, Copilot, Google AI Overviews, Google AI Mode) cite sources more often than offline models.
</Note>

## What are fan-out queries?

When an AI model with web access answers a question, it often performs **web searches** behind the scenes. These internal searches are called **fan-out queries**. Superlines tracks these to show you:

* What search terms the AI uses when answering about your topic
* Where your domain ranks for those searches
* Opportunities to improve your content for AI retrieval

## How the analysis works

<Steps>
  <Step title="Prompts are tracked">
    You add prompts to track for each brand — or let Superlines auto-discover them.
  </Step>

  <Step title="Automated testing">
    Prompts are sent to all your enabled AI engines daily.
  </Step>

  <Step title="Responses are collected">
    Full responses are captured and stored.
  </Step>

  <Step title="Brand detection">
    Responses are scanned for mentions using your brand name and variations.
  </Step>

  <Step title="Citation extraction">
    All URLs referenced in responses are parsed and tracked.
  </Step>

  <Step title="Metrics are calculated">
    Brand Visibility, Citation Rate, Share of Voice, Average Position, and more.
  </Step>

  <Step title="Insights are generated">
    Trends, alerts, and recommendations are surfaced.
  </Step>
</Steps>


# Introduction
Source: https://docs.superlines.io/getting-started/introduction

Superlines is an AI Search Intelligence platform for enterprise marketing teams and marketing agencies.

Superlines is an **AI Search Intelligence** platform built primarily for enterprise marketing teams and marketing agencies. It measures how brands appear across AI Search — mentions, position, share of voice, citations, sources and competitive performance — and gives teams the underlying data to see where visibility is gained or lost.

As AI platforms such as ChatGPT, Copilot, Gemini, Perplexity, and Google AI Overviews shape how people discover brands, Superlines gives marketing organizations one source of truth across brands, markets, languages and AI engines.

## What does Superlines do?

| Capability | What it means |
| - | - |
| **AI Search Monitoring** | Track how often your brand is mentioned in responses from ChatGPT, Copilot, Gemini, Perplexity, Google AI Overviews, Google AI Mode, Claude, Grok, Mistral, and DeepSeek. |
| **Competitor Benchmarking** | See how your Share of Voice compares to competitors across every LLM. |
| **Citation Tracking** | Know when and where your website is cited as a source in AI responses. |
| **Content Opportunities** | Discover topics and queries where you can improve your AI visibility. |
| **Prompt Management** | Track the questions your audience asks AI — and measure your performance on each one. |
| **Team Collaboration** | Role-based access so your whole team can monitor and act. |

## Who Superlines is for

Superlines is built for two primary audiences:

* **Enterprise marketing teams** — measure and improve AI Search visibility across brands, products and markets. Brand, SEO, content, communications, digital, growth and analytics teams share one source of truth for how AI platforms represent the company, how competitors perform and which sources influence answers.
* **Marketing agencies** — manage AI Search visibility across client portfolios from one platform. Benchmark competitors, identify opportunities and report progress across brands, markets and AI engines.

Superlines is particularly strong for organizations that manage multiple markets, brands, languages and AI engines, large prompt sets, many competitors and centralized reporting across a complex marketing organization.

Self-service plans are available for smaller teams that need the same intelligence at lower data volumes. Those plans do not change who the platform is built for.

Use Superlines where your team already works. Connect through [MCP](/mcp/overview) to tools such as Claude, ChatGPT, Cursor and n8n, or use the [API](/api/authentication) to bring granular AI Search data into your own dashboards, applications and reporting workflows.

See [Use Cases](/getting-started/use-cases) for how these teams work in the platform.

## Supported AI platforms

Superlines monitors responses across these AI engines (availability depends on your plan):

<CardGroup>
  <Card title="ChatGPT" icon="message-bot">OpenAI</Card>
  <Card title="Copilot" icon="microsoft">Microsoft</Card>
  <Card title="Perplexity" icon="magnifying-glass">Research-focused</Card>
  <Card title="AI Overviews" icon="google">Google Search</Card>
  <Card title="AI Mode" icon="google">Google Search</Card>
  <Card title="Gemini" icon="sparkles">Google</Card>
  <Card title="Grok" icon="bolt">xAI</Card>
  <Card title="Claude" icon="brain">Anthropic</Card>
  <Card title="Mistral" icon="wind">Mistral AI</Card>
  <Card title="DeepSeek" icon="microchip">DeepSeek</Card>
</CardGroup>

## Next steps

<CardGroup>
  <Card title="Quick Start" icon="play" href="/getting-started/quickstart">
    Set up your first brand in under 5 minutes.
  </Card>

  <Card title="Core Concepts" icon="book" href="/getting-started/concepts">
    Understand the key concepts behind the platform.
  </Card>

  <Card title="Use Cases" icon="users" href="/getting-started/use-cases">
    See how enterprise marketing teams and agencies use Superlines.
  </Card>

  <Card title="Prompt Strategy" icon="bullseye" href="/account/prompt-strategy">
    Build your prompt portfolio from multiple data sources.
  </Card>
</CardGroup>


# Quick Start
Source: https://docs.superlines.io/getting-started/quickstart

Get up and running with Superlines in 5 minutes.

This guide walks you through the essential steps to start monitoring your brand's AI search visibility.

<Steps>
  <Step title="Create your account">
    Sign up at [superlines.io](https://superlines.io) and verify your email. You'll be guided through the onboarding flow.
  </Step>

  <Step title="Set up your organization">
    Enter your organization name and website. This creates the top-level container for all your brands, team members, and subscription.
  </Step>

  <Step title="Create your first brand">
    During onboarding, configure your first brand:

    * **Brand name** — the name you want to monitor (e.g. "Superlines")
    * **Website** — your primary URL
    * **Business Type** — your business model (e.g. B2B, B2C)
    * **Markets** — one or more country/language combinations
    * **Competitors** — up to 4 direct competitors
  </Step>

  <Step title="Validate your prompts">
    Superlines automatically discovers relevant prompts for your brand. Review the suggested prompts and add any custom ones. These are the questions your audience asks AI — like *"What is the best AI visibility tool?"*
  </Step>

  <Step title="Wait for initial data">
    Your first analytics appear within **24–48 hours** as Superlines tests your prompts across multiple AI engines.
  </Step>

  <Step title="Invite your team">
    Go to **Team Management** and invite colleagues:

    * **Admin** — full platform access (no billing)
    * **Member** — read-only analytics access
  </Step>
</Steps>

<Tip>
  Start with 20–50 core prompts and gradually expand based on what you learn. Quality over quantity.
</Tip>

## What to check first

Once data starts flowing, head to the **AI Search Visibility** page to see:

* **Brand Visibility** — what % of AI responses mention your brand
* **Brands by Brand Visibility** — how your brand compares to competitors in a ranked table
* **Citation Rate** — how often your website is cited in responses
* **Prompt-level performance** — how each tracked prompt performs across AI engines

## Next steps

<CardGroup>
  <Card title="Core Concepts" icon="book" href="/getting-started/concepts">
    Learn the key concepts behind the platform.
  </Card>

  <Card title="Key Metrics" icon="chart-line" href="/metrics/key-metrics">
    Understand the metrics that drive your strategy.
  </Card>

  <Card title="MCP Server" icon="plug" href="/mcp/setup">
    Set up AI-assisted analytics.
  </Card>
</CardGroup>


# Use Cases
Source: https://docs.superlines.io/getting-started/use-cases

How enterprise marketing teams and marketing agencies use Superlines for AI Search intelligence.

Superlines is an AI Search Intelligence platform for enterprise marketing teams and marketing agencies. It is especially suited to organizations that measure AI Search across multiple brands, markets, languages, competitors and AI engines.

## Enterprise marketing teams

**Best for:** Enterprise and multi-market brands that need one source of truth for how AI platforms represent the company.

Brand, SEO, content, communications, digital, growth and analytics teams use Superlines inside the broader marketing organization. They are users of the same platform, not separate products.

### The challenge

Large organizations already have marketing messages, FAQ databases, support themes, keyword research and competitive intelligence. The harder problem is understanding how that knowledge shows up in AI Search — across brands, countries, languages and engines — and reporting it in one place.

### How they use Superlines

<Steps>
  <Step title="Measure visibility across brands and markets">
    Create a [brand](/account/brands) for each market, product line or business unit. Compare them in [Cross-Brand Analytics](/analytics/cross-brand-analytics) so leadership sees one view across the organization.
  </Step>

  <Step title="Import existing knowledge as prompts">
    Use assets you already have as the prompt set:

    | Data source | Example prompts |
    | - | - |
    | **Marketing messages** | Core value propositions rephrased as questions |
    | **FAQ databases** | Customer questions you already answer on your site |
    | **Support tickets** | Common issues and questions from real customers |
    | **Keyword research** | High-volume search queries converted to conversational prompts |
    | **Competitive intelligence** | Questions where competitors are strong |

    Import via [bulk upload](/account/prompts#bulk-import), [Google Search Console](/integrations/google-search-console), or the [MCP server](/mcp/overview).
  </Step>

  <Step title="Give each team the metric they own">
    Share dashboards across the marketing organization. Labels keep large prompt sets organized by market, product, campaign or priority.

    | Team | Primary focus |
    | - | - |
    | **Brand** | Brand Visibility, Sentiment Score, Share of Voice |
    | **SEO and content** | Citations, sources, fan-out queries, content opportunities |
    | **Communications and PR** | How AI represents the company, sentiment, competitor mentions |
    | **Digital and growth** | High-intent prompts, engine-level performance, market differences |
    | **Marketing analytics** | Trends, centralized reporting, API and Looker Studio exports |
  </Step>

  <Step title="Act on competitive and source intelligence">
    Use [fan-out query analysis](/metrics/competitive-intelligence) and citation data to see which sources AI engines rely on, where competitors outperform you, and which prompts are the largest visibility opportunities.
  </Step>
</Steps>

<Tip>
  Enable [prompt variations](/account/prompt-variations) to test how different phrasings of the same question affect brand visibility. This is useful when a global brand needs consistent measurement across markets and languages.
</Tip>

***

## Marketing agencies

**Best for:** Agencies managing AI Search visibility for multiple clients, brands or markets.

### How they use Superlines

* **One brand per client** — each with its own prompts, competitors, markets and analytics
* **Portfolio reporting** — [Cross-Brand Analytics](/analytics/cross-brand-analytics) for a view across the client book, then drill into a single brand
* **Labels** — group prompts by client, campaign, market or priority
* **Centralized reporting** — scheduled exports, Looker Studio, or the API for client dashboards
* **MCP in the tools the team already uses** — query a client's visibility from Claude, ChatGPT, Cursor or an n8n workflow without building a separate report each time
* **Team roles** — give a client read-only access to their brand while the agency keeps admin control

***

## Use Superlines outside the platform

Enterprise teams and agencies rarely want another dashboard as the only place the data lives.

* **MCP** — bring Superlines intelligence into Claude, ChatGPT, Cursor, n8n and other MCP clients. Ask for share of voice by market, citation sources, or a weekly digest in the assistant or workflow you already use. See the [MCP overview](/mcp/overview).
* **API** — pull mentions, position, share of voice, citations and prompt-level results into internal dashboards, warehouses and reporting. See [API authentication](/api/authentication).

***

## Shared workflows

These workflows apply to both enterprise marketing teams and agencies:

<CardGroup>
  <Card title="Prompt Strategy" icon="bullseye" href="/account/prompt-strategy">
    Build a prompt portfolio from the data sources you already have.
  </Card>

  <Card title="Competitive Intelligence" icon="magnifying-glass" href="/metrics/competitive-intelligence">
    Use fan-out queries and citations to see which sources and competitors shape AI answers.
  </Card>

  <Card title="MCP and API" icon="plug" href="/mcp/overview">
    Use Superlines data in Claude, ChatGPT, Cursor, n8n and your own reporting.
  </Card>

  <Card title="Cross-Brand Analytics" icon="chart-line" href="/analytics/cross-brand-analytics">
    Compare brands, markets and client portfolios in one view.
  </Card>
</CardGroup>


# Google Analytics
Source: https://docs.superlines.io/integrations/google-analytics

Connect Google Analytics 4 to detect LLM traffic to your website and compare AI vs organic referrals.

The Google Analytics integration connects GA4 to Superlines so you can detect and measure traffic coming from LLMs and AI-powered search.

## What you get

* **LLM traffic detection** — identify website visits referred by AI assistants like ChatGPT, Copilot, and Perplexity
* **AI vs organic comparison** — see how AI-referred traffic compares to traditional organic search
* **Engagement from AI referrals** — measure how visitors from AI sources interact with your site

## Connect Google Analytics

<Steps>
  <Step title="Go to Integrations">
    Open the **Integrations** page from the sidebar and make sure the correct brand is selected.
  </Step>

  <Step title="Click Connect on Google Analytics">
    Find the **Google Analytics** card and click **Connect**. A popup window opens to begin Google authorization.
  </Step>

  <Step title="Authorize access">
    Sign in with the Google account that has access to the GA4 property you want to connect. Grant the requested permissions.
  </Step>

  <Step title="Select a property">
    After authorization, choose the GA4 property from the dropdown. Properties are listed as **Account / Property (ID)**.
  </Step>

  <Step title="Save">
    Click **Connect Selected Properties** to finish. Your Google Analytics data will begin syncing.
  </Step>
</Steps>

## Connecting both GA and Search Console

If you authorize Google with both scopes, you can select a GA4 property and a Search Console site at the same time. Both integrations will be saved in a single step.

## Changing the connected property

1. Open **Integrations** and find the connected Google Analytics card.
2. Click **Configure** to reopen the property selector.
3. Choose the new property and save.

## Disconnecting

Click **Disconnect** on the Google Analytics card to remove the integration. Your historical Superlines data is not affected.

<Info>
  Only GA4 properties are supported. Universal Analytics properties are not available.
</Info>


# Google Search Console
Source: https://docs.superlines.io/integrations/google-search-console

Import real user search queries and automatically discover relevant prompts to track in AI search.

The Google Search Console integration imports real search queries from GSC into Superlines and uses them to automatically identify relevant prompts for AI search monitoring.

## What you get

* **Automatic prompt discovery** — Superlines analyzes your GSC queries and identifies which ones are relevant to track as AI prompts
* **Real user queries as prompts** — import actual search terms people use to find your site and monitor them across AI engines
* **Query-to-prompt mapping** — understand which traditional search queries also appear as AI prompts

## Connect Google Search Console

<Steps>
  <Step title="Go to Integrations">
    Open the **Integrations** page from the sidebar and make sure the correct brand is selected.
  </Step>

  <Step title="Click Connect on Search Console">
    Find the **Search Console** card and click **Connect**. A popup window opens to begin Google authorization.
  </Step>

  <Step title="Authorize access">
    Sign in with the Google account that has access to the Search Console property you want to connect. Grant the requested permissions.
  </Step>

  <Step title="Select a site">
    After authorization, choose the Search Console site (e.g. `https://example.com`) from the dropdown.
  </Step>

  <Step title="Save">
    Click **Connect Selected Properties** to finish. Your Search Console data will begin syncing.
  </Step>
</Steps>

## Changing the connected site

If you need to switch to a different Search Console property:

1. Open **Integrations** and find the connected Search Console card.
2. Click **Configure** to reopen the property selector.
3. Choose the new site and save.

## Disconnecting

Click **Disconnect** on the Search Console card to remove the integration. Your historical Superlines data is not affected.

<Info>
  The Google authorization popup must not be blocked by your browser. If you see a "popup blocked" warning, allow popups for analytics.superlines.io and try again.
</Info>

## Using GSC data strategically

Google Search Console data is one of several sources for building a comprehensive prompt portfolio. Focus on:

* **High-impression, low-click queries** — these may indicate users getting AI-powered answers instead of clicking through to your site
* **Queries with rising impressions** — emerging topics where you should establish AI search visibility early
* **Brand-adjacent queries** — searches related to your brand that could become common AI prompts

For a full guide on combining GSC data with other sources, see [Prompt Strategy](/account/prompt-strategy).


# Looker Studio
Source: https://docs.superlines.io/integrations/looker-studio

Connect your Superlines data to Looker Studio for custom dashboards and reports.

Superlines integrates with **Looker Studio** (formerly Google Data Studio), so you can build custom dashboards and reports that combine AI search visibility data with your other marketing data.

## What you can do

* **Custom dashboards** — build branded reports with your Superlines data
* **Cross-platform reporting** — combine AI visibility data with Google Analytics, Search Console, and more
* **Automated reports** — schedule email delivery of dashboards to stakeholders
* **Team sharing** — share interactive dashboards with your team

## Setup

<Steps>
  <Step title="Get your API key">
    Go to **Organization Settings → API Keys** and generate a key.
  </Step>

  <Step title="Open Looker Studio">
    Navigate to [lookerstudio.google.com](https://lookerstudio.google.com)
  </Step>

  <Step title="Add a data source">
    Click **Create → Data Source** and search for the **Superlines** connector.
  </Step>

  <Step title="Authenticate">
    Enter your API key when prompted.
  </Step>

  <Step title="Select your brand">
    Choose the brand you want to visualize.
  </Step>

  <Step title="Build your report">
    Drag and drop Superlines metrics into charts, tables, and scorecards.
  </Step>
</Steps>

## Available data fields

| Field category | Examples |
| - | - |
| **Metrics** | Brand Visibility, SOV, Citation Rate, Mentions, Sentiment |
| **Dimensions** | AI Engine, Date, Prompt, Label, Country, Language |
| **Competitor data** | Competitor names, competitor visibility, competitor citations |
| **Citation data** | Cited domains, cited URLs, citation counts |

## Dashboard ideas

<CardGroup>
  <Card title="Executive summary" icon="chart-pie">
    Scorecards for Brand Visibility, SOV, and Citation Rate with week-over-week trends.
  </Card>

  <Card title="Engine comparison" icon="chart-bar">
    Side-by-side performance across ChatGPT, Copilot, Perplexity, and AI Overviews.
  </Card>

  <Card title="Competitor leaderboard" icon="trophy">
    Ranked table of all brands by SOV with sparkline trends.
  </Card>

  <Card title="Topic performance" icon="tags">
    Heatmap of visibility by topic/label across engines.
  </Card>
</CardGroup>

<Info>
  The Looker Studio connector is available on all paid plans.
</Info>

## Resources

* [Looker Studio documentation](https://support.google.com/looker-studio)
* [Superlines connector setup guide](https://analytics.superlines.io/looker-studio)


# Integrations Overview
Source: https://docs.superlines.io/integrations/overview

Connect your tools and services to enhance AI visibility tracking in Superlines.

Superlines integrates with popular tools so you can enrich your AI search visibility data, receive alerts, and build custom reports.

## Available integrations

<CardGroup>
  <Card title="Google Search Console" icon="magnifying-glass" href="/integrations/google-search-console">
    Import real user queries and automatically discover relevant prompts to track in AI search.
  </Card>

  <Card title="Google Analytics" icon="chart-column" href="/integrations/google-analytics">
    Detect LLM-referred traffic to your website and compare AI vs organic visits.
  </Card>

  <Card title="Slack" icon="comment" href="/integrations/slack">
    Get real-time alerts and weekly summaries delivered to a Slack channel.
  </Card>

  <Card title="Looker Studio" icon="chart-pie" href="/integrations/looker-studio">
    Build custom dashboards that combine Superlines data with your other marketing data.
  </Card>
</CardGroup>

## How integrations work

Integrations in Superlines are configured **per brand**. When you connect an external service, you choose which brand the data flows into. This means you can connect different Google Analytics properties to different brands.

<Steps>
  <Step title="Select a brand">
    Navigate to **Integrations** and make sure the correct brand is selected in the sidebar.
  </Step>

  <Step title="Connect a service">
    Click **Connect** on the integration card and complete the OAuth authorization flow.
  </Step>

  <Step title="Choose properties">
    For Google integrations, select the specific property or site you want linked to the brand.
  </Step>

  <Step title="Start using enriched data">
    Connected data appears automatically in your dashboards, metrics, and reports.
  </Step>
</Steps>

## Permissions

Only **Owners** and **Admins** can manage integrations. Team members with the **Member** role are redirected away from the Integrations page.

<Info>
  You can disconnect any integration at any time from the Integrations page without losing your historical Superlines data.
</Info>


# Slack
Source: https://docs.superlines.io/integrations/slack

Receive AI visibility alerts and summaries directly in a Slack channel.

The Slack integration sends notifications to a channel in your Slack workspace, keeping your team informed about AI search visibility changes without leaving Slack.

## What you get

* **Real-time opportunity alerts** — get notified when new AI visibility opportunities are detected
* **Weekly performance summaries** — automated recaps of brand visibility, citations, and share of voice
* **Brand sentiment notifications** — stay informed about sentiment changes in AI responses that mention your brand

## Connect Slack

<Steps>
  <Step title="Go to Integrations">
    Open the **Integrations** page from the sidebar.
  </Step>

  <Step title="Click Connect on Slack">
    Find the **Slack** card and click **Connect**. A popup opens to authorize the Superlines Slack app.
  </Step>

  <Step title="Choose a channel">
    Select the Slack workspace and channel where notifications should be posted.
  </Step>

  <Step title="Authorize">
    Click **Allow** to complete the connection.
  </Step>
</Steps>

Once connected, the integration card shows the workspace name and channel.

## Disconnecting

Click **Disconnect** on the Slack card in the Integrations page. Notifications will stop immediately.

<Warning>
  Slack is connected at the **organization** level, not per brand. Disconnecting Slack removes it for all brands in the organization.
</Warning>

<Info>
  You can change the notification channel by disconnecting and reconnecting Slack with a different channel selected.
</Info>


# MCP Server Overview
Source: https://docs.superlines.io/mcp/overview

Bring Superlines AI Search intelligence into Claude, ChatGPT, Cursor, n8n and other tools your marketing team already uses.

Use Superlines where your team already works. The **Superlines MCP Server** implements the [Model Context Protocol](https://modelcontextprotocol.io) (MCP), so enterprise marketing teams and agencies can use the same AI Search intelligence inside the assistants and workflows they already have.

Connect through MCP to tools such as Claude, ChatGPT, Cursor and n8n. Ask questions about mentions, position, share of voice, citations and competitors in natural language, or manage brands and prompts from those tools. The [REST API](/api/authentication) covers the same need when the destination is a dashboard, warehouse or internal application.

## What can you do with it?

<CardGroup>
  <Card title="Analytics & Reporting" icon="chart-bar">
    Ask for brand audits, weekly performance trends, share of voice comparisons, and sentiment analysis — all in plain language.
  </Card>

  <Card title="Content Strategy" icon="pen-to-square">
    Find content opportunities, research keywords, get prompt suggestions, and identify gaps where competitors are winning.
  </Card>

  <Card title="Competitor Intelligence" icon="magnifying-glass">
    See who your top competitors are, how they compare, which prompts they dominate, and analyze their winning URLs.
  </Card>

  <Card title="Website Optimization" icon="wrench">
    Audit any webpage for AI search readiness, check Schema.org markup, and get specific improvement recommendations.
  </Card>

  <Card title="Account Management" icon="gear">
    Create new brands, add tracking prompts, manage labels, and pause or unpause prompts — all through natural language.
  </Card>
</CardGroup>

## Example prompts

Once connected, you can ask questions like:

* *"Show me my brand visibility trends for the last 8 weeks"*
* *"Which competitors are gaining traction and on what topics?"*
* *"Find content opportunities where I have low visibility"*
* *"Audit my homepage for AI search readiness"*
* *"Generate a strategic action plan to improve my AI search visibility"*
* *"What are my best performing prompts?"*
* *"Create a new brand for superlines.io"*
* *"Add these 10 prompts to my brand and label them 'product'"*
* *"Pause all prompts labeled 'seasonal'"*

## Available tools

The MCP server exposes **27 specialized tools** across these categories:

| Category | Tools | Purpose |
| - | - | - |
| **Brand Management** | `list_brands`, `get_brand_details` | List and inspect your brands |
| **Core Metrics** | `analyze_metrics`, `get_citation_data`, `analyze_brand_mentions`, `get_query_data`, `get_weekly_performance` | Retrieve analytics data |
| **Webpage Analysis** | `webpage_audit`, `webpage_analyze_technical`, `webpage_analyze_content`, `schema_optimizer` | Analyze and optimize web pages |
| **Competitive Analysis** | `get_fanout_query_insights`, `get_competitive_gap`, `get_top_cited_url_per_prompt`, `get_best_performing_prompt`, `get_period_comparison` | Competitive intelligence |
| **Advanced Analytics** | `get_competitor_insights`, `analyze_sentiment`, `find_content_opportunities`, `get_analytics_summary` | Deep analytics |
| **Strategic** | `generate_strategic_action_plan` | AI-powered recommendations |
| **Account Management** | `create_brand`, `get_brand_setup_status`, `add_prompts`, `pause_prompts`, `unpause_prompts`, `update_prompt_labels` | Create brands, manage prompts |

See the [Available Tools](/mcp/tools) page for complete documentation.

## Requirements

* **Paid plan** — Starter, Growth, or Enterprise
* **API key** — generated from Organization Settings
* **MCP client** — Claude (Desktop, Code, or claude.ai), ChatGPT, Cursor, n8n, Windsurf, Lovable, VS Code, or any MCP-compatible application

<Info>
  Most MCP tools are **read-only** and never modify your account. The account management tools (brand creation, prompt management) require **write permissions** on your API key — see the [Setup Guide](/mcp/setup#api-key-permissions) for details.
</Info>

<CardGroup>
  <Card title="Setup Guide" icon="play" href="/mcp/setup">
    Connect your MCP client in under 5 minutes.
  </Card>

  <Card title="Automation Workflows" icon="robot" href="/mcp/workflows">
    Build automated pipelines for prompt discovery, competitive intelligence, and content production.
  </Card>
</CardGroup>


# Getting Reliable Numbers from Claude
Source: https://docs.superlines.io/mcp/prompting-guide

How to ask Claude (or any MCP client) for Superlines data so the answer matches the dashboard — with copy-paste prompts and the questions to avoid.

Claude does not calculate anything. It calls a Superlines tool, receives a JSON payload, and writes it up. Every mismatch between "what Claude said" and "what the dashboard shows" comes from one of three things: Claude picked a different date range, Claude picked a different tool than the dashboard view you are comparing against, or Claude summarised across two payloads that were not comparable. This page shows how to remove all three.

<Note>
  **Rule of thumb:** tell Claude the brand, the exact dates, and the metric name as it appears in the dashboard. Then ask it to quote the `dataFreshness.latest_response_at` timestamp. If that timestamp matches the "Data updated" indicator in the dashboard, both are reading the same data.
</Note>

## The four things every data question needs

| Include | Why | Example |
| - | - | - |
| **Exact brand name** | Without it, several tools return *all your brands combined* and Claude may not notice the warning. | `for the brand "Acme Insurance"` |
| **Explicit start and end date** | MCP tools default to *the last 30 days including today*. The dashboard defaults to *the last 28 days ending yesterday*. Different windows, different numbers. | `from 2026-08-15 to 2026-09-14` |
| **End date = yesterday** | Today's collection is still running. The dashboard hides today unless you toggle "Include current date". | `ending yesterday (2026-09-14)` |
| **The metric name from the dashboard** | "Visibility", "mentions" and "share" each map to different fields. Naming the metric stops Claude picking a neighbour. | `Brand Visibility`, `Share of Voice`, `Citation Rate` |

## Copy-paste prompts

### Match a dashboard KPI

```
Using Superlines, for the brand "Acme Insurance": call analyze_metrics with
startDate 2026-08-18, endDate 2026-09-14, metrics brand_visibility and citation_rate,
granularity total. Report the exact values and the dataFreshness.latest_response_at
timestamp from the response. Do not round.
```

Compare against the dashboard with the same dates and **Include current date** off.

### Brand leaderboard (Brands by Brand Visibility)

```
Using Superlines, for the brand "Acme Insurance": call analyze_brand_mentions with
startDate 2026-08-18, endDate 2026-09-14, consolidate true, limit 20.
List the brands with their mention_count in a table, exactly as returned.
```

`consolidate: true` merges name variants ("OP", "OP Ryhmä", "OP Pohjola") the same way the dashboard does. Without it you get raw extracted names and the list will not match.

### Period-over-period change

```
Using Superlines, for the brand "Acme Insurance": call get_period_comparison with periodDays 28.
For each metric, show current value, previous value and the change, using only the
numbers in the response. State both date ranges the tool used.
```

### Per-engine breakdown

```
Using Superlines, for the brand "Acme Insurance": call analyze_metrics with
startDate 2026-08-18, endDate 2026-09-14, metrics brand_visibility,
groupBy llm_service. Show one row per engine, values as returned.
```

### Which engines show which competitors

```
Using Superlines, for the brand "Acme Insurance": call get_visibility_heatmap with
startDate 2026-08-18, endDate 2026-09-14. Show the table exactly as returned
and tell me which engine has the lowest visibility for Acme Insurance.
```

### A verifiable summary for a client

```
Using Superlines, for the brand "Acme Insurance" from 2026-08-18 to 2026-09-14:
1. analyze_metrics → brand_visibility, citation_rate, mentions, share_of_voice
2. analyze_brand_mentions with consolidate true, limit 10
3. get_period_comparison with periodDays 28
Write a one-page summary. Every number must come from a tool response. After each
number, put the tool name in brackets. End with the dataFreshness.latest_response_at
timestamp so I can check it against the dashboard.
```

## Questions to avoid, and what to ask instead

<AccordionGroup>
  <Accordion title="Don't: “How is Acme Insurance doing?”">
    No dates, no metric. Claude will pick the tool's default window (30 days including today) and whichever metric it finds first. The result cannot be checked against anything.

    **Ask instead:** *"For Acme Insurance, what was Brand Visibility from 2026-08-18 to 2026-09-14? Use analyze\_metrics."*
  </Accordion>

  <Accordion title="Don't: “Compare this month to last month”">
    "This month" is ambiguous (calendar month vs. last 30 days) and today is partial. Claude may also call two tools with slightly different windows and compare them.

    **Ask instead:** *"Call get\_period\_comparison with periodDays 28"* — one call, two aligned windows, or give both date ranges explicitly.
  </Accordion>

  <Accordion title="Don't: “What's our visibility?” when you mean the leaderboard">
    `analyze_brand_mentions` returns a `brand_visibility` field that is the brand's **share of all mentions**, not the dashboard's Brand Visibility (share of responses that mention the brand). They are different metrics with the same name.

    **Ask instead:** For the KPI, use `analyze_metrics` → `brand_visibility`. For the leaderboard, use `analyze_brand_mentions` and read `mention_count` and `share_of_voice`.
  </Accordion>

  <Accordion title="Don't: “Add up the daily numbers”">
    Daily Brand Visibility values are percentages of different denominators. Summing or averaging them does not reproduce the period total.

    **Ask instead:** call the same tool with `granularity: total` for the period.
  </Accordion>

  <Accordion title="Don't: “Roughly how much…” / “About what percent…”">
    This invites rounding and estimation. The server instructs Claude to report exact values; asking for approximations works against that.

    **Ask instead:** *"Report the exact values returned. Do not round."*
  </Accordion>

  <Accordion title="Don't: ask about a brand without naming it">
    If your API key has access to several brands and you omit the name, tools return everything combined and add a `warning` field. Claude should relay it, but the numbers will already be in the conversation.

    **Ask instead:** always say *for the brand "…"* using the exact name from `list_brands`.
  </Accordion>

  <Accordion title="Don't: mix MCP output with a screenshot from a different day">
    Collection runs continuously. A dashboard screenshot from the morning and an MCP answer from the afternoon can legitimately differ.

    **Ask instead:** compare `dataFreshness.latest_response_at` in the MCP response with the dashboard's **Data updated** indicator. Same timestamp, same data.
  </Accordion>
</AccordionGroup>

## Reading `dataFreshness`

Every analytics tool response now includes:

```json theme={null}
"dataFreshness": {
  "latest_response_at": "2026-09-15T05:41:12.000Z",
  "latest_response_date": "2026-09-15",
  "hours_since_latest": 9,
  "computed_at": "2026-09-15T14:36:02.114Z",
  "domain_ids": ["abc123"]
}
```

* `latest_response_at` is the newest AI response collected for the brand. The dashboard shows the same value as **Data updated … ago** in the filter bar and as **Last collected** inside the date picker.
* If `hours_since_latest` is above 36, collection is behind. Numbers are still correct for the data that exists, but the most recent day may be incomplete. Claude is instructed to warn you.
* Two answers are only comparable if their `latest_response_at` values match.

## Setting expectations with clients

If you present Superlines data to your own clients:

1. Pull the numbers with one of the prompts above, with explicit dates.
2. Open the dashboard with the same dates and **Include current date** off.
3. Check that **Data updated** matches `latest_response_at`.
4. Quote the date range and the "data as of" timestamp on the slide.

If a number still differs after those steps, send us the prompt, the tool response, and a dashboard screenshot — that is a defect on our side, not a usage problem.


# MCP Setup Guide
Source: https://docs.superlines.io/mcp/setup

Connect Superlines to Claude, ChatGPT, Cursor, n8n and other MCP clients so marketing teams can use AI Search intelligence where they already work.

## Prerequisites

Before you begin, make sure you have:

1. A **paid Superlines plan** (Starter, Growth, or Enterprise)
2. An **API key** from Organization Settings (format: `sl_live_...`) — not required for OAuth on Claude.ai
3. An MCP-compatible client installed

## Connection methods

| Method | Protocol | Best for |
| - | - | - |
| **OAuth** | OAuth 2.0 + SSE | Claude.ai — no API key needed |
| **SSE (URL-based)** | Server-Sent Events | Claude Desktop, Cursor, ChatGPT, n8n, Windsurf, Lovable, most clients |
| **Local npx** | Standard I/O | Cursor, Claude Desktop, VS Code — runs locally on your machine |
| **CLI** | Varies | Claude Code — add servers from the terminal |

**SSE connection URL:**

```
https://mcpsse.superlines.io?token=YOUR_API_KEY
```

Replace `YOUR_API_KEY` with your actual API key.

## Choose your platform

<CardGroup>
  <Card title="Claude.ai" icon="globe" href="#claude-ai">
    OAuth — no API key needed
  </Card>

  <Card title="Claude Desktop" icon="desktop" href="#claude-desktop">
    SSE or local npx
  </Card>

  <Card title="Claude Code" icon="terminal" href="#claude-code">
    CLI — one command setup
  </Card>

  <Card title="Cursor" icon="code" href="#cursor">
    SSE or local npx
  </Card>

  <Card title="Cursor Cloud" icon="cloud" href="#cursor-cloud">
    SSE — remote agents
  </Card>

  <Card title="Windsurf" icon="wind" href="#windsurf">
    SSE or local npx
  </Card>

  <Card title="Lovable" icon="heart" href="#lovable">
    SSE via Settings UI
  </Card>

  <Card title="ChatGPT" icon="message" href="#chatgpt">
    Connector URL
  </Card>

  <Card title="n8n" icon="diagram-project" href="#n8n">
    MCP Client node
  </Card>

  <Card title="VS Code" icon="window" href="#vs-code">
    SSE or local npx
  </Card>
</CardGroup>

***

## Claude.ai

<Info>
  **Method:** OAuth — no API key needed. You authorize access with your Superlines account.
</Info>

<Steps>
  <Step title="Open Claude.ai settings">
    Go to [claude.ai](https://claude.ai) → **Settings** → **Connectors** → **Add custom connector**.
  </Step>

  <Step title="Enter the server details">
    * **Name:** `Superlines`
    * **URL:** `https://mcpsse.superlines.io`
    * Open **Advanced settings** and set **OAuth Client ID** to `anthropic-claude`
    * Leave **OAuth Client Secret** blank
    * Click **Add**
  </Step>

  <Step title="Authorize access">
    Claude will redirect you to the Superlines authorization page. Sign in with your Superlines account, review the requested permissions, select which brands to share, and click **Authorize Access**.
  </Step>

  <Step title="Start using Superlines">
    Back in Claude, start a new conversation and try: *"List my Superlines brands"*
  </Step>
</Steps>

<Tip>
  OAuth connections are scoped to the brands you select during authorization. You can revoke access at any time from your Superlines account settings.
</Tip>

***

## Claude Desktop

<Info>
  **Method:** SSE (recommended) or local npx. Both use the `claude_desktop_config.json` config file.
</Info>

<Steps>
  <Step title="Open Claude Desktop settings">
    Go to **Settings → Developer → Edit Config** to open:

    ```
    ~/Library/Application Support/Claude/claude_desktop_config.json
    ```

    On Windows:

    ```
    %APPDATA%\Claude\claude_desktop_config.json
    ```
  </Step>

  <Step title="Add the Superlines server">
    <Tabs>
      <Tab title="SSE (recommended)">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "url": "https://mcpsse.superlines.io?token=YOUR_API_KEY",
              "transport": "sse"
            }
          }
        }
        ```
      </Tab>

      <Tab title="Local npx">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "command": "npx",
              "args": ["-y", "@superlines/mcp-server"],
              "env": {
                "SUPERLINES_API_KEY": "YOUR_API_KEY"
              }
            }
          }
        }
        ```

        <Note>Requires **Node.js** installed on your machine.</Note>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Restart Claude Desktop">
    Save the file and fully restart the app for changes to take effect.
  </Step>
</Steps>

***

## Claude Code

<Info>
  **Method:** CLI — add the server directly from your terminal with one command.
</Info>

<Steps>
  <Step title="Add the Superlines server">
    Run one of these commands in your terminal:

    <Tabs>
      <Tab title="SSE (recommended)">
        ```bash theme={null}
        claude mcp add --transport sse superlines \
          "https://mcpsse.superlines.io?token=YOUR_API_KEY"
        ```
      </Tab>

      <Tab title="Local npx">
        ```bash theme={null}
        claude mcp add --transport stdio \
          --env SUPERLINES_API_KEY=YOUR_API_KEY \
          superlines -- npx -y @superlines/mcp-server
        ```

        <Note>Requires **Node.js** installed on your machine.</Note>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Verify the connection">
    ```bash theme={null}
    claude mcp list
    ```

    You should see `superlines` in the list of connected servers.
  </Step>

  <Step title="Start using Superlines">
    In your Claude Code session, try: *"List my Superlines brands"*
  </Step>
</Steps>

<Tip>
  Use the `--scope` flag to control where the configuration is stored:

  * `--scope local` (default) — private to you, this project only
  * `--scope project` — shared via `.mcp.json`, checked into git
  * `--scope user` — available across all your projects
</Tip>

***

## Cursor

<Info>
  **Method:** SSE (recommended) or local npx via `.cursor/mcp.json`.
</Info>

<Steps>
  <Step title="Open MCP settings">
    Go to **Cursor Settings → Tools & MCP → Add new MCP server**, or create/edit the `.cursor/mcp.json` file in your project root.
  </Step>

  <Step title="Add the Superlines server">
    <Tabs>
      <Tab title="SSE (recommended)">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "url": "https://mcpsse.superlines.io?token=YOUR_API_KEY",
              "transport": "sse"
            }
          }
        }
        ```
      </Tab>

      <Tab title="Local npx">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "command": "npx",
              "args": ["-y", "@superlines/mcp-server"],
              "env": {
                "SUPERLINES_API_KEY": "YOUR_API_KEY"
              }
            }
          }
        }
        ```

        <Note>Requires **Node.js** installed on your machine.</Note>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Restart Cursor">
    Restart Cursor for the changes to take effect. You should see the Superlines server listed under **Tools & MCP** in settings.
  </Step>
</Steps>

***

## Cursor Cloud

<Info>
  **Method:** SSE only — cloud agents run in remote VMs, so local npx is not available.
</Info>

Since Cursor Cloud agents run in a remote environment, only the SSE (URL-based) connection method works.

<Steps>
  <Step title="Add the Superlines server">
    Add the following to your `.cursor/mcp.json` file in your project root:

    ```json theme={null}
    {
      "mcpServers": {
        "superlines": {
          "url": "https://mcpsse.superlines.io?token=YOUR_API_KEY",
          "transport": "sse"
        }
      }
    }
    ```
  </Step>

  <Step title="Commit the config">
    Commit the `.cursor/mcp.json` file to your repository so it's available in the cloud environment.
  </Step>

  <Step title="Verify in cloud session">
    When using a Cursor Cloud agent, the Superlines tools will be available automatically. Try asking: *"List my Superlines brands"*
  </Step>
</Steps>

<Warning>
  Local npx transport does not work with Cursor Cloud agents. Always use the SSE method for cloud environments.
</Warning>

***

## Windsurf

<Info>
  **Method:** SSE URL or local npx via `mcp_config.json`. You can also browse the built-in MCP Marketplace.
</Info>

<Steps>
  <Step title="Open MCP configuration">
    In Windsurf, click the **Plugins** icon in the sidebar → **Manage plugins** → **View raw config**, or edit the file directly:

    ```
    ~/.codeium/windsurf/mcp_config.json
    ```
  </Step>

  <Step title="Add the Superlines server">
    <Tabs>
      <Tab title="SSE (recommended)">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "serverUrl": "https://mcpsse.superlines.io?token=YOUR_API_KEY"
            }
          }
        }
        ```
      </Tab>

      <Tab title="Local npx">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "command": "npx",
              "args": ["-y", "@superlines/mcp-server"],
              "env": {
                "SUPERLINES_API_KEY": "YOUR_API_KEY"
              }
            }
          }
        }
        ```

        <Note>Requires **Node.js** installed on your machine.</Note>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Refresh Windsurf">
    Save the file and restart Windsurf, or refresh the MCP plugins from the sidebar.
  </Step>
</Steps>

<Tip>
  Windsurf supports environment variable interpolation in MCP configs. You can use `${env:SUPERLINES_API_KEY}` in the `serverUrl` to reference environment variables instead of hardcoding your key.
</Tip>

***

## Lovable

<Info>
  **Method:** SSE URL via Settings UI. Custom MCP servers are available on all paid Lovable plans.
</Info>

<Steps>
  <Step title="Open connector settings">
    Go to **Settings → Connectors → Personal connectors** and click **New MCP server**.
  </Step>

  <Step title="Enter the server details">
    * **Server name:** `Superlines`
    * **Server URL:** `https://mcpsse.superlines.io?token=YOUR_API_KEY`
    * **Authentication:** Select **No authentication** (the API key is included in the URL)
  </Step>

  <Step title="Add the server">
    Click **Add**. The Superlines server now appears in your list of personal connectors.
  </Step>

  <Step title="Use in your project">
    In any Lovable project, the Superlines tools are now available to the AI agent. Try prompting: *"Use Superlines to show my brand visibility"*
  </Step>
</Steps>

<Tip>
  You can also use **Bearer token** authentication. Set the Server URL to `https://mcpsse.superlines.io` and enter your API key (format: `sl_live_...`) as the bearer token.
</Tip>

***

## ChatGPT

<Info>
  **Method:** MCP Connector via Settings. Requires a **ChatGPT Plus or Pro** subscription with Developer Mode enabled.
</Info>

<Steps>
  <Step title="Enable Developer Mode">
    In [ChatGPT](https://chatgpt.com), go to your profile → **Settings → Apps & Connectors → Advanced settings** (at the bottom of the page) and toggle on **Developer Mode**.
  </Step>

  <Step title="Create a connector">
    Go to **Settings → Apps & Connectors** and click **Create**.
  </Step>

  <Step title="Enter the server details">
    * **Name:** `Superlines`
    * **Description:** `AI Search Intelligence — mentions, share of voice, citations and competitors`
    * **MCP Server URL:** `https://mcpsse.superlines.io?token=YOUR_API_KEY`
  </Step>

  <Step title="Confirm and create">
    Check the confirmation box and click **Create**. You should see the available Superlines tools listed.
  </Step>

  <Step title="Start using Superlines">
    Open a new ChatGPT conversation, click the **+** button near the message composer, select **More**, then choose the **Superlines** connector. Try: *"List my Superlines brands"*
  </Step>
</Steps>

<Note>
  ChatGPT connectors require a remote HTTPS endpoint. Local npx servers do not work with ChatGPT. MCP support in ChatGPT is currently in beta and may evolve — check [OpenAI's documentation](https://developers.openai.com/apps-sdk/deploy/connect-chatgpt/) for the latest setup steps.
</Note>

***

## n8n

<Info>
  **Method:** MCP Client node over SSE. Use this when a marketing or agency workflow should pull Superlines data on a schedule — for example a Monday digest per brand or market into Slack, Teams or Sheets.
</Info>

<Steps>
  <Step title="Add an MCP Client node">
    In an n8n workflow, add an **MCP Client** node. If an AI Agent should call Superlines as a tool, use **MCP Client Tool** instead and connect it to the agent.
  </Step>

  <Step title="Set the server">
    * **Server Transport:** `SSE`
    * **Endpoint:** `https://mcpsse.superlines.io`
    * **Authentication:** Bearer Auth, with your Superlines API key (`sl_live_...`) as the token

    You can also put the key in the URL and set authentication to **None**:

    ```
    https://mcpsse.superlines.io?token=YOUR_API_KEY
    ```
  </Step>

  <Step title="Choose a tool and run it">
    n8n loads the Superlines tools from the server. Pick one — for example list brands or a metrics query — fill in the parameters, and execute the node.

    A typical agency or enterprise workflow is: schedule trigger → MCP Client (period comparison for each brand or market) → Slack, email or a spreadsheet.
  </Step>
</Steps>

<Note>
  Parameter names in n8n are **Server Transport**, **Endpoint** (shown as MCP Endpoint URL in current n8n docs) and **Authentication**. If SSE does not connect, try the HTTP transport against the same endpoint. The API key must belong to a paid plan.
</Note>

***

## VS Code

<Info>
  **Method:** SSE or local npx. The config format depends on your MCP extension (e.g., GitHub Copilot, Continue, Cline).
</Info>

<Steps>
  <Step title="Open your MCP configuration">
    Check your MCP extension's documentation for the config file location. Common locations include `.vscode/mcp.json` in your project root or the extension's global settings.
  </Step>

  <Step title="Add the Superlines server">
    <Tabs>
      <Tab title="SSE (recommended)">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "url": "https://mcpsse.superlines.io?token=YOUR_API_KEY",
              "transport": "sse"
            }
          }
        }
        ```
      </Tab>

      <Tab title="Local npx">
        ```json theme={null}
        {
          "mcpServers": {
            "superlines": {
              "command": "npx",
              "args": ["-y", "@superlines/mcp-server"],
              "env": {
                "SUPERLINES_API_KEY": "YOUR_API_KEY"
              }
            }
          }
        }
        ```

        <Note>Requires **Node.js** installed on your machine.</Note>
      </Tab>
    </Tabs>
  </Step>

  <Step title="Restart VS Code">
    Restart VS Code for the changes to take effect.
  </Step>
</Steps>

***

## Other MCP clients

Any MCP-compatible client can connect to Superlines using the SSE endpoint:

```
https://mcpsse.superlines.io?token=YOUR_API_KEY
```

Add this URL as an SSE transport server in your client's MCP configuration. Consult your client's documentation for the specific config format.

For clients that support local process-based connections, use the npm package:

```bash theme={null}
npx -y @superlines/mcp-server
```

Set the `SUPERLINES_API_KEY` environment variable to your API key.

***

## API key permissions

By default, API keys have **read-only** access — they can query analytics data but cannot modify your account. To use the [Account Management](/mcp/tools#account-management) tools (creating brands, adding prompts, etc.), you need to enable write permissions on your API key.

### How to enable write permissions

<Steps>
  <Step title="Go to Organization Settings">
    Open **Organization Settings → API Keys** in the Superlines dashboard.
  </Step>

  <Step title="Edit your API key">
    Click on the API key you use for MCP and open its permissions settings.
  </Step>

  <Step title="Enable write permissions">
    Toggle on the permissions you need:

    * **`write:brands`** — allows creating new brands/domains
    * **`write:prompts`** — allows adding, pausing, unpausing prompts and managing labels
  </Step>

  <Step title="Save and reconnect">
    Save your changes. No client restart is needed — permissions take effect immediately.
  </Step>
</Steps>

### Available permissions

| Permission | Grants access to |
| - | - |
| `read` | All analytics and reporting tools (default, always included) |
| `write:brands` | `create_brand`, `get_brand_setup_status` |
| `write:prompts` | `add_prompts`, `pause_prompts`, `unpause_prompts`, `update_prompt_labels` |

<Info>
  Write operations are **rate-limited** and **audit-logged** for security. Brand creation is limited to 5 per hour, and prompt operations to 100 per hour per organization. All write actions are recorded in your organization's audit log.
</Info>

<Warning>
  Deletion is intentionally not supported via MCP to prevent accidental data loss. Use `pause_prompts` to temporarily disable prompts instead.
</Warning>

***

## Verify the connection

After setting up your client:

1. Start a new conversation
2. Ask: *"List my Superlines brands"*
3. The AI should call the `list_brands` tool and show your brands

If it works, try:

* *"Show me my brand visibility for the last 30 days"*
* *"Who are my top competitors?"*
* *"Find content opportunities where I have low visibility"*

## Troubleshooting

| Issue | Solution |
| - | - |
| "Tool not found" | Restart your MCP client after configuration changes |
| "Authentication failed" | Verify your API key is correct and starts with `sl_live_` |
| "No data returned" | Make sure you have at least one brand with tracked prompts |
| "Connection timeout" | Check your internet connection; the SSE endpoint is at `mcpsse.superlines.io` |
| "Plan not supported" | MCP requires a paid plan (Starter, Growth, or Enterprise) |
| "Command not found" (npx) | Ensure Node.js is installed and `npx` is in your PATH |
| Server hangs on connection | Try the SSE method instead of npx — it's more reliable for remote connections |
| ChatGPT connector not visible | Confirm Developer Mode is enabled and you have a Plus or Pro subscription |
| Lovable tools not available | Make sure your Lovable plan supports custom MCP servers (paid plans) |
| Windsurf server not loading | Check that `mcp_config.json` is valid JSON — trailing commas cause silent failures |

<Warning>
  **Never share your API key publicly.** If compromised, revoke it immediately in Organization Settings and generate a new one.
</Warning>

## Resources

* [NPM Package: @superlines/mcp-server](https://www.npmjs.com/package/@superlines/mcp-server)
* [MCP Protocol Specification](https://modelcontextprotocol.io)
* [Available Tools Reference](/mcp/tools)
* Support: [support@superlines.io](mailto:support@superlines.io)


# Available Tools
Source: https://docs.superlines.io/mcp/tools

Complete reference of all MCP tools available in the Superlines MCP server.

The Superlines MCP server exposes **30 tools** organized into categories. Most tools are read-only. The [Account Management](#account-management) tools can create brands and manage prompts — these require **write permissions** on your API key.

<Tip>
  When asking about a **specific brand**, always include the brand name in your query. The AI assistant will pass it to the `brands` parameter automatically. If you omit brands, data for **all** brands in your organization is returned.
</Tip>

## Brand Management

### `list_brands`

Lists all brands available in your account. **Always call this first** to get exact brand names for other tools.

* **Parameters:** None
* **Returns:** List of brands with IDs, names, websites, country, language, industry, and competitors

### `get_brand_details`

Get detailed information about a specific brand including competitors, industry, and market settings.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |

***

## Core Metrics

### `analyze_metrics`

Get comprehensive analytics data including Brand Visibility, Citation Rate, Mentions, Share of Voice, and Citations.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands`. Omit to return all brands combined. |
| `metrics` | string\[] | No | `brand_visibility`, `citation_rate`, `mentions`, `share_of_voice`, `citations`, `share_of_citations`, `responses`, `tests` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD). Defaults to last 30 days. |
| `granularity` | string | No | `total` (default), `daily`, `weekly`, `monthly` |
| `groupBy` | string\[] | No | `llm_service`, `llm_model`, `country`, `language`, `topic` |
| `filters` | object | No | `llmService`, `llmModel`, `country`, `language`, `topic`, etc. |

### `get_citation_data`

Analyze citations at domain or URL level. Shows which domains and URLs are cited in AI responses.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `aggregateBy` | string | No | `domain` (default) or `url` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `granularity` | string | No | `total`, `daily`, `weekly`, `monthly` |
| `groupBy` | string\[] | No | `llm_service`, `llm_model`, `country`, etc. |
| `filters` | object | No | `llmService`, `llmModel`, `promptId`, `domain`, `country`, `topic` |
| `limit` | number | No | Max results (default: 100) |

### `analyze_brand_mentions`

Analyze brand mentions and competitor presence. Shows mention counts, sentiment scores, and share of voice.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `granularity` | string | No | `total`, `daily`, `weekly`, `monthly` |
| `groupBy` | string\[] | No | `brand`, `llm_service`, `country`, `topic` |
| `filters` | object | No | `llmService`, `llmModel`, `promptId`, `brand`, `sentiment` |
| `minMentions` | number | No | Minimum mentions to include (default: 1) |
| `limit` | number | No | Max brands to return (default: 50) |

### `get_query_data`

Analyze search queries and their metadata. Shows query text, prompt count, intent, user journey stage, and brand mentions.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `promptId` | string | No | Filter by a specific prompt ID |
| `groupBy` | string\[] | No | `query`, `intent`, `userJourneyStage`, `category` |
| `filters` | object | No | `intent`, `userJourneyStage`, `category`, `llmService`, `country`, `naicsCode` |
| `sortBy` | string | No | `prompts` (default) or `query` |
| `minVolume` | number | No | Minimum search volume (default: 0) |
| `limit` | number | No | Max queries (default: 100) |

### `get_weekly_performance`

Get weekly performance trends over the last N weeks with week-over-week changes.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `weeksBack` | number | No | Number of weeks (default: 4, max: 52) |
| `metrics` | string\[] | No | Metrics to track (defaults to all core metrics) |

***

## Webpage Analysis

### `webpage_audit`

Comprehensive LLM-friendliness audit — technical SEO, content quality, metadata, schemas, writing quality, citations, and tone.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `url` | string | Yes | Full URL (must include `https://`) |
| `includeCodeSnippets` | boolean | No | Generate actionable code snippets (default: true) |

### `webpage_analyze_technical`

Focused technical analysis — metadata, structured data (JSON-LD, microdata), heading structure, accessibility, and technical SEO.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `url` | string | Yes | Full URL (must include `https://`) |
| `includeCodeSnippets` | boolean | No | Generate technical code snippets (default: true) |

### `webpage_analyze_content`

Focused content analysis — heading format, content organization, data citations, tone, writing quality, and marketing messages.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `url` | string | Yes | Full URL (must include `https://`) |

### `schema_optimizer`

Optimize Schema.org structured data. Returns quality score, proposed schemas, and implementation code.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `url` | string | Yes | Full URL (must include `https://`) |
| `includeCodeSnippets` | boolean | No | Generate implementation code (default: true) |

***

## Competitive Analysis

### `get_fanout_query_insights`

Analyze fan-out queries — the web searches LLMs perform when answering user questions. Shows which queries are most common and where your domain ranks.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `limit` | number | No | Max fan-out queries to analyze (default: 5) |

### `get_competitive_gap`

Find prompts where competitors lead or where you're winning. Includes visibility comparison and optional AI recommendations.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `promptId` | string | No | Scope to a single prompt (expanded to its active cluster) |
| `promptIds` | string\[] | No | Scope to multiple prompt IDs (expanded to clusters) |
| `aggregateBy` | string | No | `cluster` (aggregate together, default) or `prompt` (per-prompt results) |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD). Defaults to last 30 days. |
| `includeAIAnalysis` | boolean | No | Include AI analysis of competitor's top-ranking URL (default: false) |

### `get_top_cited_url_per_prompt`

Get the #1 cited URL for each tracked prompt.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `limit` | number | No | Max prompts to analyze (default: 20) |

### `get_best_performing_prompt`

Find your best performing prompts based on visibility or citation rate.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `metric` | string | No | `visibility` (default), `citations`, or `both` |

### `get_period_comparison`

Compare current period metrics vs. previous period with trend analysis.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `periodDays` | number | No | Days per period (default: 30). Compares last N days vs. previous N days. |
| `metrics` | string\[] | No | Specific metrics to compare |

***

## Advanced Analytics

### `get_competitor_insights`

Top cited domains and most mentioned brands for your tracked prompts.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `topN` | number | No | Number of top competitors (default: 10) |

### `analyze_sentiment`

Sentiment of AI responses mentioning your brand. Shows positive/neutral/negative breakdown.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `groupBy` | string | No | `llm_service`, `topic`, or `country` |

### `find_content_opportunities`

Topics with high query volume but low brand visibility — your biggest growth opportunities.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `minMentions` | number | No | Minimum response count to consider a topic (default: 3) |

### `get_analytics_summary`

High-level analytics summary with overall metrics and top topics.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD) |
| `groupBy` | string | No | `llm_service`, `country`, `language`, `topic`, or `date` |

***

## Strategic Tools

### `generate_strategic_action_plan`

Generate a priority-ranked action plan with improvement potential scores.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brands` | string\[] | Recommended | Brand names from `list_brands` |
| `focusArea` | string | No | `all` (default), `visibility`, `citations`, `sentiment`, `content_gaps` |
| `maxRecommendations` | number | No | Max items (default: 5, max: 10) |
| `competitorUrls` | string\[] | No | Specific competitor URLs to analyze |
| `startDate` / `endDate` | string | No | Date range (YYYY-MM-DD). Defaults to last 30 days. |

***

## Account Management

<Warning>
  These tools modify your account. They require **write permissions** on your API key. See [API Key Permissions](/mcp/setup#api-key-permissions) for setup instructions.
</Warning>

### `create_brand`

Create a new brand/domain in your organization. Triggers brand analysis (website scanning, competitor detection, industry classification) and prompt generation in the background. Returns immediately — use `get_brand_setup_status` to poll for completion.

**Permission required:** `write:brands`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `brandName` | string | Yes | Brand or company name |
| `website` | string | Yes | Website URL (e.g. `https://superlines.io`) |
| `country` | string | No | Target country (e.g. `United States`) |
| `language` | string | No | Target language (e.g. `English`) |
| `competitors` | array | No | List of competitors (`name` required, `website` optional). Auto-detected if omitted. |
| `industry` | string | No | Industry vertical (auto-detected if omitted) |

<Note>
  Brand creation is **idempotent** — if a brand with the same domain already exists, the existing brand is returned instead of creating a duplicate.
</Note>

### `get_brand_setup_status`

Check the progress of brand setup after `create_brand`. Returns analysis status, prompt generation status, and whether the brand is ready for analytics.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | The domain ID returned by `create_brand` |

### `update_brand`

Update brand details such as topic, company description, country, language, industry, or competitors. Use this to correct or improve auto-detected values from brand analysis.

**Permission required:** `write:brands`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |
| `topic` | string | No | Concise topic (2–4 words, e.g. `AI Search Analytics`) |
| `companyDescription` | string | No | 1–2 sentence company elevator pitch |
| `country` | string | No | Target country (e.g. `Finland`) |
| `language` | string | No | Target language (e.g. `English`) |
| `industry` | string | No | Industry vertical (e.g. `SaaS`) |
| `competitors` | array | No | Updated competitor list — replaces existing list |

<Note>
  Setting `topic` triggers automatic recomputation of the standardized topic used in analytics.
</Note>

### `list_prompts`

List tracked prompts for a brand or across all brands. Returns prompt text, status, labels, and metadata. Also returns your organization's quota usage.

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | No | Filter by a specific domain. Omit to list prompts across all domains. |
| `status` | string | No | `active`, `paused`, or `all` (default) |
| `label` | string | No | Filter by label |
| `limit` | number | No | Max prompts to return (default: 200, max: 500) |

### `add_prompts`

Add tracking prompts to a brand. Prompts are the search queries that Superlines monitors across AI platforms (ChatGPT, Gemini, Perplexity, etc). Each prompt is created as active and included in the next crawl cycle. Maximum 50 prompts per call.

**Permission required:** `write:prompts`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |
| `prompts` | array | Yes | Array of prompt objects (1-50) |
| `prompts[].text` | string | Yes | The prompt text (max 500 chars) |
| `prompts[].label` | string | No | Label/category (e.g. `product`, `brand`) |
| `prompts[].intent` | string | No | Search intent (e.g. `informational`, `commercial`) |

<Note>
  Duplicate prompts are automatically detected and skipped. Your organization's prompt quota is enforced.
</Note>

### `pause_prompts`

Pause active prompts so they stop being crawled. Paused prompts are not deleted and can be reactivated with `unpause_prompts`. Filter by specific IDs or by label.

**Permission required:** `write:prompts`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |
| `promptIds` | string\[] | No | Specific prompt IDs to pause |
| `label` | string | No | Pause all active prompts with this label |

### `unpause_prompts`

Reactivate paused prompts so they resume being crawled. Checks your prompt quota before reactivating.

**Permission required:** `write:prompts`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |
| `promptIds` | string\[] | No | Specific prompt IDs to unpause |
| `label` | string | No | Unpause all paused prompts with this label |

### `update_prompt_labels`

Add, remove, or set labels on prompts. Labels help organize prompts into categories for filtering in analytics.

**Permission required:** `write:prompts`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |
| `promptIds` | string\[] | Yes | Prompt IDs to update (max 100) |
| `label` | string | Yes | The label to add, remove, or set |
| `action` | string | No | `set` (default — replaces all labels), `add`, or `remove` |

### `delete_prompts`

Permanently delete tracked prompts by ID or by label filter. Deleted prompts cannot be recovered.

**Permission required:** `write:prompts`

| Parameter | Type | Required | Description |
| - | - | - | - |
| `domainId` | string | Yes | Domain ID from `list_brands` |
| `promptIds` | string\[] | No | Specific prompt IDs to delete (max 100). Use this OR `label`. |
| `label` | string | No | Delete all prompts with this label. Use this OR `promptIds`. |

<Warning>
  Deletion is **permanent and irreversible**. Use `pause_prompts` if you may want to reactivate the prompts later. Historical response data linked to the prompts is retained for reporting.
</Warning>


# Automation Workflows
Source: https://docs.superlines.io/mcp/workflows

Automate AI Search reporting and monitoring with the Superlines MCP server in n8n and other workflow tools.

The MCP server lets enterprise marketing teams and agencies use Superlines intelligence in the workflows they already run. A common pattern is an n8n workflow that pulls a period comparison per brand or market and posts it to Slack, Teams or a reporting channel. The same server also connects to Claude, ChatGPT and Cursor.

## What you can automate

<CardGroup>
  <Card title="Prompt Discovery" icon="magnifying-glass">
    Automatically find and add relevant prompts from external data sources.
  </Card>

  <Card title="Competitive Monitoring" icon="chart-bar">
    Surface prompts where you're losing to competitors and trigger content actions.
  </Card>

  <Card title="Content Pipeline" icon="pen-to-square">
    Feed AI search insights directly to content production agents.
  </Card>

  <Card title="Reporting" icon="file-lines">
    Generate automated performance reports and alerts.
  </Card>
</CardGroup>

***

## Prompt discovery workflows

### External data source to Superlines

Use AI agents to discover prompts from external sources and add them to tracking automatically.

**Flow:**

1. An AI agent scrapes an external source (SERP results, trending articles, Reddit threads, industry forums)
2. The agent extracts relevant questions and topics
3. The agent calls `list_brands` to identify the target brand
4. The agent calls `add_prompts` to add discovered prompts with appropriate labels

**Example prompt to your AI agent:**

```
Using the Superlines MCP server:
1. List my brands to find the domain ID for "My Brand"
2. Research trending topics in [your industry] using SERP data
3. Generate 10 conversational prompts from those topics
4. Add them to my brand with the label "trend-discovery"
```

### Google Search Console to strategic prompts

Combine GSC data with Superlines analytics to find high-value prompts:

**Flow:**

1. Connect [Google Search Console](/integrations/google-search-console) to import queries
2. Use an AI agent to call `get_query_data` and identify high-impression queries
3. Cross-reference with `analyze_metrics` to find queries where visibility is low
4. Prioritize these as strategic prompts with `update_prompt_labels`

***

## Competitive intelligence workflows

### Find prompts where you're losing

Automatically identify fan-out queries where competitors outrank you.

**Example prompt to your AI agent:**

```
Using the Superlines MCP server for brand "My Brand":
1. Call get_fanout_query_insights to see fan-out query performance
2. Identify queries where my domain ranks poorly or isn't present
3. Call get_competitive_gap to see which competitors lead on those topics
4. For the top 5 gaps, call webpage_audit on the competitor's cited page
5. Summarize: what content am I missing, and what should I create?
```

### Audit competitor content automatically

When a competitor is winning on a specific prompt, reverse-engineer their strategy:

**Flow:**

1. Call `get_top_cited_url_per_prompt` to find which URLs are winning
2. Call `webpage_audit` on those competitor URLs to get detailed content analysis
3. Call `webpage_audit` on your own competing pages for comparison
4. Generate a content brief based on the gaps

***

## Content production pipeline

### Agent-to-agent content collaboration

The most powerful workflow connects Superlines data directly to a content production agent. This creates a closed-loop system: **discover opportunities → create content → monitor results**.

**Architecture:**

```
┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Superlines MCP │────▶│  Orchestrator    │────▶│  Content Agent  │
│  (Intelligence) │     │  Agent           │     │  (Production)   │
│                 │◀────│                  │◀────│                 │
└─────────────────┘     └──────────────────┘     └─────────────────┘
```

**Step-by-step flow:**

<Steps>
  <Step title="Identify opportunities">
    The orchestrator calls `find_content_opportunities` to discover topics with high query volume but low brand visibility.
  </Step>

  <Step title="Analyze competitive landscape">
    For each opportunity, call `get_fanout_query_insights` and `get_top_cited_url_per_prompt` to understand what content currently wins.
  </Step>

  <Step title="Audit top-performing content">
    Call `webpage_audit` and `webpage_analyze_content` on the winning URLs to extract what makes them successful.
  </Step>

  <Step title="Generate content brief">
    The orchestrator synthesizes the data into a content brief: target prompts, fan-out queries to optimize for, competitive gaps to exploit, and content structure recommendations.
  </Step>

  <Step title="Content agent produces draft">
    A content production agent (Claude, GPT, or a custom agent) creates content based on the brief. This could feed directly into a CMS publication queue.
  </Step>

  <Step title="Monitor results">
    After publishing, use `get_weekly_performance` and `get_period_comparison` to track whether the new content improves visibility for the target prompts.
  </Step>
</Steps>

***

## Reporting and monitoring workflows

### Automated weekly reports

**Example prompt to your AI agent:**

```
Using the Superlines MCP server:
1. Call get_weekly_performance for brand "My Brand" over the last 4 weeks
2. Call get_period_comparison to compare this month vs last month
3. Call get_best_performing_prompt to find top 5 prompts
4. Call find_content_opportunities for the top 3 gaps
5. Format a weekly executive summary with key trends and recommended actions
```

### Strategic action plans

**Example prompt to your AI agent:**

```
Using Superlines MCP:
1. Call generate_strategic_action_plan with focusArea "all" for "My Brand"
2. For each recommended action, call the relevant analysis tool to gather supporting data
3. Produce a prioritized action plan with specific content recommendations
```

***

## Prompt management at scale

The MCP server makes prompt management efficient for large portfolios:

| Task | MCP approach |
| - | - |
| **List current prompts** | Call `list_prompts` to see all tracked prompts with status and quota usage |
| **Add prompts in bulk** | Call `add_prompts` with up to 50 prompts per call, with labels |
| **Pause seasonal prompts** | Call `pause_prompts` with a label filter (e.g., `summer-campaign`) |
| **Reactivate after season** | Call `unpause_prompts` with the same label filter |
| **Reorganize labels** | Call `update_prompt_labels` to reclassify prompts |
| **Remove stale prompts** | Call `delete_prompts` with a label or ID list to permanently remove low-value prompts |
| **Discover + add in one flow** | Chain external data scraping with `add_prompts` in a single agent workflow |

***

## Best practices for MCP automation

<AccordionGroup>
  <Accordion title="Start simple, then automate">
    Run workflows manually with your AI assistant first. Once you understand the flow and validate the outputs, then build automated pipelines.
  </Accordion>

  <Accordion title="Use labels for workflow tracking">
    Label prompts added by automated workflows (e.g., `mcp-auto`, `trend-discovery`) so you can measure their effectiveness separately from manually curated prompts.
  </Accordion>

  <Accordion title="Combine MCP tools in sequence">
    The most powerful workflows chain multiple MCP tools. For example: `list_brands` → `get_fanout_query_insights` → `webpage_audit` → `generate_strategic_action_plan`.
  </Accordion>

  <Accordion title="Keep a human in the loop for content publishing">
    Let agents generate content briefs and drafts, but have a human review before publishing. AI search visibility is a strategic asset — quality matters more than speed.
  </Accordion>

  <Accordion title="Monitor automated prompts">
    Regularly review prompts added by automated workflows. Pause or remove any that aren't generating valuable insights after 2-4 weeks of tracking.
  </Accordion>
</AccordionGroup>

***

## Getting started

<CardGroup>
  <Card title="MCP Setup" icon="plug" href="/mcp/setup">
    Connect the MCP server to your AI assistant.
  </Card>

  <Card title="Available Tools" icon="wrench" href="/mcp/tools">
    Full reference of all 30 MCP tools.
  </Card>
</CardGroup>


# AI Crawler Analytics
Source: https://docs.superlines.io/metrics/ai-crawler-analytics

Track and analyze AI bot traffic on your website to understand how AI engines crawl your content.

AI Crawler Analytics (labeled **Bot Traffic** in the platform) lets you see exactly which AI bots and crawlers are visiting your website, how often they visit, and which pages they're crawling. This gives you a direct signal of how AI engines interact with your content — complementing the response-based metrics like Brand Visibility and Citation Rate.

<Note>
  AI Crawler Analytics requires installing a lightweight tracking script on your website. No data will appear until the script is live.
</Note>

## Why it matters

AI search engines like ChatGPT, Perplexity, and Claude crawl your website to fetch content for their responses. Tracking this traffic helps you:

* **Confirm your content is being crawled** — if AI bots aren't visiting, they can't cite you
* **Identify which pages attract AI attention** — double down on content that bots frequently visit
* **Detect crawl pattern changes** — a drop in AI crawler visits may foreshadow a drop in visibility
* **Separate AI traffic from human traffic** — understand your true audience mix

## Setup

<Steps>
  <Step title="Navigate to AI Crawler Analytics">
    Open the **AI Crawler Analytics** page from the sidebar in the Superlines dashboard.
  </Step>

  <Step title="Copy the tracking script">
    The page displays a tracking script unique to your organization. Click **Copy** to copy the snippet.
  </Step>

  <Step title="Add the script to your website">
    Paste the script into the `<body>` of every page you want to track. It works with any website — static HTML, WordPress, Next.js, Webflow, or any other platform.

    ```html theme={null}
    <!-- Superlines Bot Analytics -->
    <script>
    (function(){
      var img = new Image(1,1);
      var params = "tid=YOUR_TRACKING_ID&url=" + encodeURIComponent(window.location.href);
      if (document.referrer) params += "&ref=" + encodeURIComponent(document.referrer);
      img.src = "https://tracking.superlines.io?" + params;
      img.style.cssText = "position:absolute;left:-9999px;visibility:hidden";
      img.alt = "";
      document.body.appendChild(img);
    })();
    </script>
    <noscript>
      <img src="https://tracking.superlines.io?tid=YOUR_TRACKING_ID"
           width="1" height="1"
           style="position:absolute;left:-9999px;visibility:hidden" alt="">
    </noscript>
    ```
  </Step>

  <Step title="Verify installation">
    After deploying, return to the AI Crawler Analytics page. The status indicator will show **Active** once the first visits are recorded — typically within minutes.
  </Step>
</Steps>

<Tip>
  The tracking script is a 1×1 transparent pixel — it has zero impact on page load performance and does not use cookies or collect personal data from your visitors.
</Tip>

## How bot detection works

Superlines identifies bots using two methods:

| Method | How it works | Confidence |
| - | - | - |
| **User-Agent matching** | Compares the visitor's User-Agent string against a database of 80+ known bots | High |
| **IP attribution** | Maps the visitor's IP address to known LLM provider IP ranges (OpenAI, Anthropic, Perplexity, Mistral) | High |

IP attribution is especially valuable because some AI engines use headless browsers that don't identify themselves in the User-Agent string. By matching their IP ranges, Superlines can still attribute the visit to the correct engine.

## Bot categories

Every detected bot is classified into one of these categories:

| Category | What it includes | Examples |
| - | - | - |
| **AI Training** | Crawlers that collect data for training AI models | GPTBot, ClaudeBot, Google-Extended, CCBot, Bytespider |
| **AI Search** | Bots that fetch content for real-time AI search responses | OAI-SearchBot, PerplexityBot, Claude-SearchBot, ChatGPT-User |
| **Search Engine** | Traditional search engine crawlers | Googlebot, Bingbot, Applebot, DuckDuckBot |
| **Social Media** | Link preview bots from social platforms | FacebookBot, Twitterbot, LinkedInBot, Slackbot |
| **SEO Tool** | Crawlers from SEO analysis platforms | AhrefsBot, SemrushBot, MJ12bot, DotBot |
| **Monitoring** | Uptime and performance monitoring bots | UptimeRobot, Pingdom, GTmetrix |

<Tip>
  Use the category filter on the dashboard to focus on specific bot types like AI Training and AI Search crawlers — the ones most relevant to your AI search visibility.
</Tip>

## Dashboard overview

The AI Crawler Analytics dashboard provides several views:

### Summary cards

At the top, you'll see high-level stats for the selected date range:

* **Top Crawler** — the most active bot visiting your site
* **Top URL** — the page receiving the most bot traffic
* **Top Category** — the most common bot category
* **Top Country** — where most bot traffic originates

### Trend chart

A daily time-series showing bot visit volume over time. You can toggle between:

* **All bots** — combined view
* **Per-bot breakdown** — individual trend lines for each bot (color-coded by engine)

### Top bots

A ranked table of the most active bots, showing visit count and percentage of total bot traffic. Each bot displays its category badge and organization.

### Category breakdown

A pie chart showing the distribution of bot traffic across categories (AI Training, AI Search, Search Engine, etc.).

### Top pages

Which pages on your site are most frequently crawled by bots. This helps you understand what content AI engines find most valuable.

### Geographic distribution

Where bot traffic originates from, broken down by country. Most AI engine traffic comes from US and EU data centers, but this can reveal unexpected patterns.

## Filtering

The dashboard supports several filters to narrow down your analysis:

| Filter | Options |
| - | - |
| **Date range** | Custom date picker, presets for 7d / 30d / 90d |
| **Bot filter** | Filter to a specific bot (e.g., only GPTBot) |
| **Category filter** | Filter to a specific category (e.g., only AI Search) |
| **Country filter** | Filter by geographic origin |
| **All bots** | Toggle between showing all bots or filtering to specific categories |

## Human visitor analytics

The dashboard also provides analytics for human visitors:

* **Top referrers** — where your human traffic comes from
* **Browsers** — browser distribution (Chrome, Safari, Firefox, etc.)
* **Devices** — mobile vs. desktop vs. tablet breakdown

This gives you a complete picture of all traffic hitting your tracked pages.

## Tracking scope

Tracking IDs can be scoped at two levels:

| Level | Use case |
| - | - |
| **Organization** | One tracking ID shared across all your brands. Use this when all brands share the same website domain. |
| **Domain (brand)** | A separate tracking ID per brand. Use this when your brands have different website domains. |

If multiple brands share the same website domain, Superlines automatically shares the tracking ID between them so you don't need to install the script multiple times.

## Data and privacy

* The tracking pixel does **not** use cookies
* No personally identifiable information (PII) is collected from human visitors
* IP addresses are used only for bot attribution and geo-location, not stored long-term for human visitors
* The script has **zero impact** on Core Web Vitals and page performance
* Data is stored in BigQuery with daily partitioning for efficient querying

## FAQ

<AccordionGroup>
  <Accordion title="How long until I see data?">
    Data appears within minutes of the tracking script being live. However, AI bot crawl frequency varies — some bots visit daily, others weekly. Give it 24–48 hours for a representative sample.
  </Accordion>

  <Accordion title="Does this affect my website performance?">
    No. The tracking script loads a 1×1 transparent GIF asynchronously. It does not block page rendering, execute third-party JavaScript, or impact Core Web Vitals.
  </Accordion>

  <Accordion title="Can I block specific bots using this data?">
    Superlines only tracks bot visits — it does not block them. To block specific bots, use your website's `robots.txt` file. The data from Superlines helps you make informed decisions about which bots to allow or disallow.
  </Accordion>

  <Accordion title="Why don't I see any AI bot visits?">
    AI bots may not crawl every website frequently. Ensure the tracking script is installed on your most important content pages. If your site is new or has low domain authority, AI crawlers may visit less often. Check that your `robots.txt` is not blocking AI bots.
  </Accordion>

  <Accordion title="What's the difference between AI Training and AI Search bots?">
    **AI Training** bots (like GPTBot and ClaudeBot) crawl your site to collect data for training future AI models. **AI Search** bots (like OAI-SearchBot and PerplexityBot) fetch your content in real-time to generate search answers. AI Search bot visits are a more direct signal of your content being used in AI responses.
  </Accordion>
</AccordionGroup>

<CardGroup>
  <Card title="Key metrics" icon="chart-line" href="/metrics/key-metrics">
    Learn about Brand Visibility, SOV, and other core metrics.
  </Card>

  <Card title="Analysis process" icon="gears" href="/metrics/analysis-process">
    Understand how Superlines collects and processes AI search data.
  </Card>
</CardGroup>


# Analysis Process
Source: https://docs.superlines.io/metrics/analysis-process

How Superlines collects, processes, and analyzes your AI search data.

Understanding how data flows through Superlines helps you interpret your metrics and make better decisions.

## Daily analysis cycle

Superlines runs a daily analysis cycle for each active brand:

```
Tracked Prompts → AI Engine Testing → Response Collection → Analysis → Metrics & Insights
```

<Steps>
  <Step title="Prompt selection">
    Active prompts for the brand are queued for testing.
  </Step>

  <Step title="AI engine testing">
    Each prompt is sent to all enabled AI engines.
  </Step>

  <Step title="Response collection">
    Full responses are captured (text, HTML, citations).
  </Step>

  <Step title="Brand detection">
    Responses are scanned for brand mentions using the brand name, variations, and competitor names.
  </Step>

  <Step title="Citation extraction">
    URLs referenced in responses are parsed and tracked.
  </Step>

  <Step title="Fan-out tracking">
    Web searches performed by AI engines are captured.
  </Step>

  <Step title="Metric calculation">
    Brand Visibility, SOV, Citation Rate, and other metrics are computed.
  </Step>

  <Step title="Insight generation">
    Trends, alerts, and recommendations are created.
  </Step>
</Steps>

<Note>
  This process runs **daily** for active prompts. New prompts are queued and tested within 24 hours of being added.
</Note>

## When data becomes available

| Event | Timeline |
| - | - |
| Brand created | First results within 1–2 hours |
| Prompt added | Tested within 24 hours |
| Prompt paused/resumed | Takes effect immediately |
| Engine added/removed | Takes effect from next testing cycle |
| Data stabilization | 48 hours for reliable metrics |

## Why initial numbers may fluctuate

When you first create a brand or add new prompts, you may see numbers change rapidly:

* **First hour** — initial results come in, percentages swing with small sample sizes
* **First 24 hours** — more responses are collected, metrics begin to stabilize
* **48+ hours** — enough data for reliable metrics and trend detection

This is expected behavior. Superlines needs a representative sample of AI responses before metrics accurately reflect your brand's visibility.

## How metrics are aggregated

| Level | What it shows |
| - | - |
| **Per prompt** | How you perform on a specific question |
| **Per engine** | How you perform on a specific AI platform |
| **Per brand** | Overall brand performance across all prompts and engines |
| **Per label** | Performance for a group of prompts sharing a label |
| **Cross-brand** | Comparison across all your brands |

## Trend detection

Superlines automatically detects and surfaces:

* **Week-over-week changes** in core metrics
* **Prompt-level alerts** when visibility drops significantly
* **Competitor movements** — new competitors gaining traction
* **Content opportunities** — topics with high volume but low visibility

These are surfaced as **Signals** on prompts (Trophy, Star, Alert, etc.) and as **Next Actions** on the dashboard.

## Data retention

* All response data is retained for the lifetime of your subscription
* Historical trends are available from the moment you start tracking
* Paused prompts retain their historical data
* Deleted prompts retain aggregated metrics but remove raw response data


# Citations & Fan-out Queries
Source: https://docs.superlines.io/metrics/citations

Understand how AI engines cite your content and what searches they perform.

Citations and fan-out queries are two powerful concepts that help you understand how AI engines find and reference your content.

## What are citations?

A **citation** is a reference to a specific URL in an AI response. When an AI engine links to your website while answering a question, that's a citation.

> *"Superlines is an AI Search Intelligence platform that helps enterprise marketing teams and agencies measure how their brands appear in AI responses. [Source: superlines.io](https://superlines.io)"*

### Why citations matter

* They're a **strong signal** of content authority
* They drive **referral traffic** from AI interfaces
* They indicate the AI considers your content **trustworthy and relevant**
* They're visible to the user as a credible source

### Which engines provide citations?

| Engine | Citations? | Notes |
| - | - | - |
| **Copilot** | ✅ Yes | Frequently cites web sources |
| **Perplexity** | ✅ Yes | Always cites sources — citation-first model |
| **Google AI Overviews** | ✅ Yes | Cites sources in search results |
| **Google AI Mode** | ✅ Yes | Cites sources in AI-powered search |
| **ChatGPT** (web) | Sometimes | Cites when web search is used |
| **Grok** | Sometimes | Cites from X/web when available |
| **Gemini** | Rarely | Mostly uses training data |
| **Claude** | Rarely | Primarily training data |
| **Mistral** | Rarely | Primarily training data |
| **DeepSeek** | Rarely | Primarily training data |

## Citation tracking in Superlines

Superlines tracks citations at two levels:

### Domain-level citations

See how often **your entire domain** (e.g., `superlines.io`) is cited across all responses. This shows overall domain authority.

### URL-level citations

See which **specific pages** on your site are cited most often. This helps you understand which content is performing best.

<Tip>
  Focus on creating comprehensive, well-structured, authoritative content. AI engines cite content that directly answers the question with specific, useful information.
</Tip>

## What are fan-out queries?

When an AI engine with web access answers a question, it often performs **web searches** behind the scenes to find current information. These internal searches are called **fan-out queries**.

### Example

If a user asks: *"What are the best AI visibility tools?"*

The AI might perform these fan-out queries:

1. *"AI search visibility tools 2025"*
2. *"brand monitoring AI search"*
3. *"AI SEO tools comparison"*

### Why fan-out queries matter

* They reveal **what the AI searches for** when answering about your topic
* They show **where your domain ranks** in those searches
* They help you **optimize content** for AI retrieval specifically
* They expose **content gaps** — topics the AI searches for but you don't rank for

<Info>
  Fan-out queries are one of the most powerful signals for competitive intelligence. For a full guide on using fan-out data to analyze competitors, identify content gaps, and build a content strategy, see [Competitive Intelligence](/metrics/competitive-intelligence).
</Info>

## Improving your citations

<Tabs>
  <Tab title="Content strategies">
    1. **Create comprehensive, authoritative content** that directly answers common questions
    2. **Use clear headings and structure** so AI can easily extract relevant sections
    3. **Include specific data, statistics, and examples** that AI models value
    4. **Keep content up to date** — AI engines prefer current information
    5. **Build backlinks and domain authority** — higher-authority domains get cited more
  </Tab>

  <Tab title="Technical strategies">
    1. **Implement Schema.org markup** — use the Superlines Schema Optimizer tool
    2. **Ensure fast page load** — AI crawlers respect performance
    3. **Use clear meta descriptions** — helps AI understand page content
    4. **Structure content with FAQ sections** — maps directly to common prompts
  </Tab>
</Tabs>

## Next steps

<CardGroup>
  <Card title="Competitive Intelligence" icon="magnifying-glass" href="/metrics/competitive-intelligence">
    Use fan-out queries to reverse-engineer competitor content strategies.
  </Card>

  <Card title="MCP Automation" icon="robot" href="/mcp/workflows">
    Automate citation analysis and content pipeline workflows.
  </Card>
</CardGroup>


# Competitive Intelligence
Source: https://docs.superlines.io/metrics/competitive-intelligence

Use fan-out queries, citations, and competitor analysis to reverse-engineer what works and improve your AI search visibility.

Fan-out queries and citation data together form a powerful competitive intelligence system. They tell you exactly what content AI engines use when answering questions about your market — and where your competitors are winning.

## The competitive intelligence loop

<Steps>
  <Step title="Track prompts in your market">
    Your tracked prompts generate responses across AI engines. Each response contains data about who gets mentioned and cited.
  </Step>

  <Step title="Analyze fan-out queries">
    See what web searches AI engines perform behind the scenes. This reveals the *real* queries that drive AI citations.
  </Step>

  <Step title="Compare your position vs. competitors">
    For each fan-out query, see where your domain ranks relative to competitors. Identify where you're strong and where you're weak.
  </Step>

  <Step title="Reverse-engineer winning content">
    Analyze the top-cited URLs to understand what makes them win — then create better content for those topics.
  </Step>

  <Step title="Monitor improvement">
    Track your visibility and citation changes over time as your content improves.
  </Step>
</Steps>

***

## Fan-out queries as competitive signals

[Fan-out queries](/metrics/citations#what-are-fan-out-queries) are the web searches AI engines perform when answering a user's question. They're the bridge between AI prompts and traditional web content.

### What fan-out queries reveal

| Insight | How to use it |
| - | - |
| **Which searches drive AI answers** | Optimize your content for these specific queries |
| **Your domain's ranking per query** | Identify where you rank well vs. where you're invisible |
| **Competitor rankings** | See which competitors dominate the searches AI engines use |
| **Content gaps** | Discover fan-out queries where no one in your space has good content |

### Reading fan-out query data

When you view fan-out query insights (via the dashboard or the `get_fanout_query_insights` [MCP tool](/mcp/tools#get_fanout_query_insights)), you see:

* The **fan-out query text** — the actual search the AI engine performed
* **Your domain's position** — where your site appears in search results for that query
* **Competitor positions** — where competitors rank
* **Citation impact** — whether this query led to your domain being cited in the AI response

<Tip>
  Focus on fan-out queries where competitors rank in the top positions but you're absent or low. These are your highest-impact content opportunities.
</Tip>

***

## Competitive gap analysis

The competitive gap analysis reveals where competitors consistently outperform you.

### Using the dashboard

The **Competitive Gap** view shows prompts where:

* Competitors have high visibility but you don't
* Competitors are cited but your domain isn't
* Competitor sentiment is more positive than yours

### Using the MCP server

The `get_competitive_gap` tool provides programmatic access to gap analysis:

```
"Show me prompts where my competitors lead and I have low visibility,
 and recommend which ones to prioritize for content creation."
```

This returns prompts ranked by gap size, with AI-powered recommendations for how to close each gap.

***

## Reverse-engineering competitor content

Once you've identified where competitors win, dig into *why* they win:

### Step 1: Find the winning URLs

Use **Top Cited URL per Prompt** (dashboard or `get_top_cited_url_per_prompt` via MCP) to see which specific URLs get cited most for each prompt.

### Step 2: Audit the winning content

For each top-cited URL, analyze what makes it successful:

| Analysis tool | What it reveals |
| - | - |
| **[`analyze_competitor_url`](/mcp/tools#analyze_competitor_url)** | Comprehensive content analysis with recommendations for competing |
| **[`webpage_audit`](/mcp/tools#webpage_audit)** | Technical and content quality audit |
| **[`webpage_analyze_content`](/mcp/tools#webpage_analyze_content)** | Content structure, tone, and writing quality |
| **[`schema_optimizer`](/mcp/tools#schema_optimizer)** | Schema.org markup that may be boosting their citations |

### Step 3: Create a content brief

From your analysis, identify:

* **Content structure** — how is the winning page organized?
* **Key topics covered** — what specific questions does it answer?
* **Fan-out query alignment** — does it rank for the fan-out queries AI engines use?
* **Technical advantages** — better Schema.org markup, faster page load, clearer structure?
* **What's missing** — gaps you can fill to create an even better resource

***

## Content opportunity prioritization

Not all competitive gaps deserve equal investment. Prioritize based on:

| Factor | Higher priority | Lower priority |
| - | - | - |
| **Prompt importance** | Strategic prompts with high market relevance | Low-volume monitoring prompts |
| **Gap size** | Competitors have 80%+ visibility, you have \<20% | Small visibility differences |
| **Fan-out query opportunity** | You rank for none of the fan-out queries | You already rank for some |
| **Content feasibility** | Topic is in your expertise area | Requires new domain expertise |
| **Citation potential** | Engines that cite sources (Perplexity, Copilot) are involved | Mostly offline models |

<Info>
  Use `find_content_opportunities` via the [MCP server](/mcp/tools#find_content_opportunities) to get an automated prioritization of topics with high query volume and low brand visibility.
</Info>

***

## Automating competitive intelligence

For ongoing competitive monitoring, use [MCP automation workflows](/mcp/workflows):

1. **Weekly gap scan** — automatically identify new prompts where competitors have gained ground
2. **Content audit pipeline** — when a new competitor URL starts getting cited, automatically analyze it
3. **Strategic action plans** — generate prioritized recommendations using `generate_strategic_action_plan`

See the [MCP Automation Workflows](/mcp/workflows) page for ready-to-use workflow examples.

***

## From intelligence to action

The end-to-end flow from competitive intelligence to improved visibility:

```
Fan-out query analysis → Identify gaps → Audit competitor content
    → Create content brief → Produce content → Publish
        → Monitor in Superlines → Iterate
```

<CardGroup>
  <Card title="MCP Workflows" icon="robot" href="/mcp/workflows">
    Automate the competitive intelligence pipeline with AI agents.
  </Card>

  <Card title="Citations & Fan-out Queries" icon="link" href="/metrics/citations">
    Deep-dive into how citations and fan-out queries work.
  </Card>
</CardGroup>


# Key Metrics
Source: https://docs.superlines.io/metrics/key-metrics

Understand the core metrics that Superlines tracks for your brand.

Superlines tracks several core metrics to help you understand and optimize your brand's AI search visibility.

## Brand Visibility

**What it measures:** The percentage of AI responses that mention your brand.

```
Brand Visibility = (Responses mentioning your brand / Total responses) × 100
```

**Example:** If 100 prompts are tested and your brand appears in 30 responses, your Brand Visibility is **30%**.

| Benchmark | Rating |
| - | - |
| > 30% | Excellent |
| 20–30% | Good |
| 10–20% | Fair |
| \< 10% | Needs improvement |

<Note>
  Brand Visibility is the single most important metric — it tells you how visible your brand is when people ask AI about topics relevant to your business.
</Note>

### Dashboard level vs. per-prompt level

Brand Visibility is detected in two slightly different ways depending on where you look:

* The **dashboard KPI card and label/theme-level figures** count a response when your brand is found anywhere in the response text, including common name variants (e.g., "Yale" counts for the brand "Yale Home").
* The **per-prompt Brand Visibility** in the Prompts table and Tracked Prompts CSV export applies a stricter rule: a response counts only when your exact brand name appears in the structured list of brands extracted from that response.

Because the per-prompt rule is stricter, per-prompt percentages can all sit below the label-level figure they belong to — this is expected. To reconcile the CSV export with the dashboard KPI, divide **Brand Mentions** by **AI Responses** per prompt; those columns use the same detection as the dashboard and roll up to it exactly.

## Share of Voice (SOV)

**What it measures:** Your brand mentions as a percentage of **all brand mentions** (yours + competitors).

```
Share of Voice = (Your brand mentions / All brand mentions) × 100
```

**Example:** If there are 200 total brand mentions across all responses, and 50 are yours, your SOV is **25%**.

| Benchmark | Position |
| - | - |
| > 25% | Market leader |
| 15–25% | Strong presence |
| 5–15% | Moderate |
| \< 5% | Needs attention |

### Key difference from Brand Visibility

* **Brand Visibility** = how often you're mentioned out of all responses
* **Share of Voice** = how often you're mentioned compared to competitors

SOV tells you about your competitive position. If your SOV is low but Brand Visibility is decent, it means competitors are also being mentioned frequently.

## Citation Rate

**What it measures:** The percentage of AI responses that cite (link to) your website.

```
Citation Rate = (Responses citing your website / Total responses) × 100
```

| Benchmark | Rating |
| - | - |
| > 20% | Excellent |
| 10–20% | Good |
| 5–10% | Fair |
| \< 5% | Needs improvement |

<Tip>
  Citations are a strong signal of content authority. AI models cite sources when your content is seen as authoritative and relevant. Focus on creating comprehensive, well-structured content to improve citations.
</Tip>

## Share of Citations

**What it measures:** Your website citations as a percentage of **all citations** in branded responses.

```
Share of Citations = (Your citations / Total citations in branded responses) × 100
```

This helps you understand how your website's authority compares to competitors in terms of being used as a source.

## Mentions

The raw count of times your brand is mentioned across all AI responses. While Brand Visibility is a percentage, Mentions gives you the absolute number — useful for understanding volume and trends.

## Average Position

**What it measures:** The average rank at which your brand is mentioned in AI-generated answers. Position 1 means your brand is mentioned first, Position 2 means second, and so on.

```
Average Position = Volume-weighted average of your brand's mention order across all responses
```

**How it works:** When an AI model responds to a prompt and mentions multiple brands, the order matters. The brand mentioned first (Position 1) is typically the most prominently recommended. Superlines tracks this order for every response and calculates a volume-weighted average — days with more responses have proportionally more influence on the score, similar to how Google Search Console calculates impression-weighted position.

**Example:** If your brand appears at Position 1 in 60 responses and Position 3 in 40 responses, your Average Position is **1.8** (weighted by daily response volume).

| Benchmark | Rating |
| - | - |
| 1.0–2.0 | Excellent — consistently mentioned first |
| 2.0–3.5 | Good — among the top recommendations |
| 3.5–5.0 | Fair — mentioned but not prominently |
| > 5.0 | Needs improvement |

<Note>
  Only responses where your brand is **actually mentioned** are included in the calculation. Responses with no mention of your brand do not affect your Average Position. See Brand Visibility and Mentions for how often your brand appears. Lower is better.
</Note>

### Average Position Ranking

The leaderboard ranks **all brands** by their volume-weighted average position across your tracked prompts. This lets you see exactly where your brand stands relative to competitors — who gets mentioned first, and who gets mentioned last.

* **Rank change** shows whether your relative position improved or declined vs. the previous period
* Brands with fewer than 3 appearances are excluded to filter noise
* The ranking updates daily and supports competitor comparison in the trend chart

<Tip>
  Average Position is particularly useful for understanding *quality* of visibility. A brand can have high Brand Visibility (mentioned often) but poor Average Position (mentioned last). Improving your position means AI models see your brand as a more primary recommendation.
</Tip>

## Industry Average

**What it measures:** The average score of the brands shown in the Industry Ranking table, used as a benchmark reference on the Brand Visibility, Citation Rate, and Sentiment KPI cards.

```
Industry Average = Simple mean of the metric across the top 10 brands in the Industry Ranking
```

Which brands are included depends on the **Only my competitors** toggle in the Industry Ranking section of the Visibility Dashboard:

* **Toggle on (default):** your configured competitors plus your own brand
* **Toggle off:** all brands detected in AI responses

<Note>
  The Industry Average always includes your own brand, covers at most the top 10 brands shown in the ranking, and is unweighted — every brand counts equally regardless of how many mentions it has.
</Note>

## Sentiment Score

The average sentiment of AI responses that mention your brand:

* **Positive** — the AI presents your brand favorably
* **Neutral** — factual mention without strong sentiment
* **Negative** — the AI raises concerns about your brand

## Metrics by engine

All metrics can be filtered and broken down by AI engine. This helps you identify which engines your brand performs best on, spot engines where competitors are winning, and prioritize content strategies per platform.


# Understanding Responses
Source: https://docs.superlines.io/metrics/responses

How Superlines collects and analyzes AI responses.

Every time Superlines tests a prompt against an AI engine, it captures the full response and runs it through analysis.

## What is a response?

A **response** is the full text answer that an AI engine gives to a tracked prompt. Superlines stores and analyzes each response to extract:

* **Brand mentions** — does the response name your brand or any competitor?
* **Website citations** — does the response reference any URLs?
* **Sentiment** — is the tone positive, neutral, or negative?
* **Content structure** — headers, lists, links, and formatting

## How responses are collected

<Steps>
  <Step title="Prompt submission">
    Your tracked prompts are sent to each enabled AI engine.
  </Step>

  <Step title="Response capture">
    The AI engine generates a response. Superlines captures the full text (text, HTML, and markdown).
  </Step>

  <Step title="Storage">
    Responses are stored in BigQuery for analytics.
  </Step>

  <Step title="Analysis">
    Brand detection and citation extraction run automatically.
  </Step>
</Steps>

<Note>
  Responses are collected daily for active prompts. The testing frequency depends on your plan.
</Note>

## Brand detection

Superlines detects brand mentions using:

* Your **brand name** (normalized for consistent matching)
* **Brand name variations** you've configured
* **Brand exclude list** to avoid false positives
* **Competitor names** and their aliases

For example, if your brand is "Nike Running" and you've added "NRC" and "Nike Run Club" as variations, all three will be detected as mentions of your brand.

See [Brand Detection settings](/account/brands#brand-detection) for full configuration instructions.

## Where to view responses

| What you want | Where to find it |
| - | - |
| All responses for a prompt | Click any prompt → Response detail view |
| Full response text | Response Analysis page |
| Brand mention highlights | Response detail view (highlighted in response) |
| Citation links | Citations Analysis page |
| Sentiment breakdown | Brand Sentiment page |

## Response metrics per prompt

Each prompt in the **Tracked Prompts** table shows:

| Column | Meaning |
| - | - |
| **Total responses** | How many AI responses have been tested |
| **Brand mentions** | How many responses mention your brand |
| **Brand Visibility %** | Mentions / total responses |
| **Citations** | How many responses cite your website |
| **Branded Response Rate (BRR)** | % of responses that mention any brand |

<Tip>
  **BRR** tells you how "brand-focused" a topic is. A high BRR means brands are commonly mentioned for this query — making it competitive but valuable to win.
</Tip>

## Why numbers may fluctuate early on

When you first create a brand, numbers may change rapidly — especially in the first hour as initial responses come in. This is normal:

* **Small sample sizes** make percentages volatile
* **Each new response** has a larger relative impact
* Data **stabilizes** after 24–48 hours as more responses are collected

Give your brand at least 48 hours of data collection before drawing conclusions.


# AI Search Checker
Source: https://docs.superlines.io/tools/ai-search-checker

Analyze any webpage for AI search readiness across five key dimensions.

The AI Search Checker evaluates how well any webpage is optimized for visibility in AI-powered search results. It scores your page across five dimensions and provides prioritized recommendations to improve your AI search presence.

## What it analyzes

The tool evaluates five dimensions of AI search readiness:

| Dimension | What it checks |
| - | - |
| **SEO Metadata** | Title tags, meta descriptions, Open Graph tags, canonical URLs |
| **Structured Data** | Schema.org markup (JSON-LD, Microdata), rich result eligibility |
| **Semantic HTML5** | Proper use of headings, sections, landmarks, and semantic elements |
| **Content Quality** | Readability, depth, authority signals, E-E-A-T indicators |
| **LLM Readiness** | How well the content can be parsed and understood by AI models |

## How to use it

<Steps>
  <Step title="Enter a URL">
    Navigate to **AI Search Checker** in the Tools section of the sidebar. Enter the URL of any webpage you want to analyze.
  </Step>

  <Step title="Run the analysis">
    Click **Analyze**. The tool will crawl the page and evaluate it across all five dimensions. This typically takes 10–30 seconds.
  </Step>

  <Step title="Review your scores">
    You'll see an overall AI Search Readiness Score (0–100) along with sub-scores for Technical, Content, and Schema dimensions, each with a brief rationale.
  </Step>

  <Step title="Follow the recommendations">
    Below the scores, you'll find prioritized recommendations ranked by impact. Each recommendation explains what to fix and why it matters for AI visibility.
  </Step>
</Steps>

<Tip>
  Not sure where to start? Try the example sites (Nike, Stripe, GitHub Docs, NY Times) to see how well-known sites score and understand what good looks like.
</Tip>

## Understanding the readiness score

The overall score reflects how well-prepared your page is for AI search visibility:

| Score | Rating | Meaning |
| - | - | - |
| 80–100 | Excellent | Your page is well-optimized for AI search |
| 60–79 | Good | Solid foundation with room for improvement |
| 40–59 | Needs Work | Significant gaps that limit AI visibility |
| 0–39 | Poor | Major issues preventing AI discovery |

## The detailed analysis report

Below the score summary, you'll find a comprehensive analysis covering:

* **SEO metadata analysis** — evaluation of your title, description, and meta tags
* **Structured data review** — what Schema.org markup exists and what's missing
* **Semantic HTML5 evaluation** — how well your page uses semantic elements
* **Content quality assessment** — readability, depth, and authority signals
* **LLM readability evaluation** — how easily AI models can parse your content

## Schema optimization

The AI Search Checker includes a built-in schema analysis section that shows:

* Your current Schema.org implementation and quality score
* A summary of AI-recommended changes
* A highlighted diff view comparing your existing schema with the optimized version
* Ready-to-use JSON-LD that you can copy and paste into your page

<Info>
  For a deeper Schema.org analysis with more detailed optimization, use the dedicated [Schema Optimiser](/tools/schema-optimiser).
</Info>

## Exporting results

You can export the full analysis in two formats:

* **Markdown** — copy the complete analysis report to share with your team
* **PDF** — download a formatted report for stakeholders

The optimized JSON-LD schema can be copied as raw JSON or as an HTML `<script>` tag ready to paste into your page.

## Related tools

<CardGroup>
  <Card title="Schema Optimiser" icon="code" href="/tools/schema-optimiser">
    Deep-dive into Schema.org optimization for any URL.
  </Card>

  <Card title="Site Analyzer" icon="magnifying-glass-chart" href="/tools/site-analyzer">
    Full technical SEO audit with issue categorization.
  </Card>

  <Card title="Site Health" icon="heart-pulse" href="/website/site-health">
    Ongoing monitoring of your website's health scores.
  </Card>

  <Card title="AI Agent" icon="robot" href="/features/agent">
    Ask the Agent to audit pages and explain results.
  </Card>
</CardGroup>


# Keyword Research
Source: https://docs.superlines.io/tools/keyword-research

Research keywords with search volume, difficulty, CPC, intent, and trend data.

The Keyword Research tool helps you discover and analyze keywords relevant to your AI search strategy. Get search volume, cost-per-click, search intent, and trend data to inform your content creation and prompt tracking decisions.

## How to use it

<Steps>
  <Step title="Enter a keyword">
    Navigate to **Keyword Research** in the Tools section. Enter a keyword or phrase in the search field.
  </Step>

  <Step title="Select a country">
    Choose the target country for localized search data using the country selector.
  </Step>

  <Step title="Run the research">
    Click **Research** to analyze the keyword. Results typically appear within a few seconds.
  </Step>

  <Step title="Explore related keywords">
    Browse the Similar, Related, and Question keyword tabs to discover additional opportunities.
  </Step>
</Steps>

## Main keyword card

The hero section shows detailed data for your researched keyword:

| Data point | Description |
| - | - |
| **Search Volume** | Monthly search volume for the keyword |
| **Traffic Potential** | Estimated traffic if ranking in position 1 |
| **CPC** | Average cost per click in paid search |
| **Search Intent** | Classification: informational, commercial, navigational, or transactional |
| **12-month Trend** | Area chart showing search volume over the past year |

## Quick stats

Four cards summarize the keyword landscape:

* **Similar Keywords** — count of closely related keywords
* **Related Keywords** — count of semantically related keywords
* **Question Keywords** — count of question-format variations
* **Total Keywords** — combined total found

## Keyword tabs

### Similar Keywords

Keywords closely matching your search term — variations, long-tail versions, and synonyms.

### Related Keywords

Semantically related keywords that share topical relevance but may use different phrasing.

### Question Keywords

Question-format keywords (how, what, why, where, when, which) — these are especially valuable for AI search because they mirror how users query AI models.

<Tip>
  Question keywords are particularly valuable for AI visibility. AI models are designed to answer questions, so tracking and creating content for question-format prompts often yields the best results.
</Tip>

Each keyword tab shows a table with:

| Column | Description |
| - | - |
| **Keyword** | The keyword text |
| **Search Volume** | Monthly search volume |
| **CPC** | Cost per click |
| **Trend** | Sparkline showing 12-month search trend |
| **Intent** | Search intent classification |

## Advanced filters

Open the filter panel to narrow results:

* **Search Volume Range** — slider to set minimum and maximum volume
* **CPC Range** — slider to set CPC bounds
* **Search Intent** — checkboxes for informational, commercial, navigational, transactional
* **Trend Direction** — filter by keywords trending up, down, or stable
* **Word Count** — filter by number of words in the keyword

## Exporting data

* **Copy keywords** — copy individual keywords or all selected keywords to clipboard
* **CSV export** — download the full keyword list with all data columns

## Using keyword research for AI visibility

Keyword research connects to your broader AI search strategy:

1. **Discover tracking prompts** — find high-volume keywords to add as tracked prompts via [Prompt Import](/account/prompt-import)
2. **Inform content creation** — identify topics for [Content Generation](/content/overview) based on search demand
3. **Validate prompt strategy** — confirm your [Prompt Strategy](/account/prompt-strategy) covers the highest-value search terms
4. **Monitor trends** — spot emerging topics before competitors target them

## Related tools

<CardGroup>
  <Card title="Prompt Import" icon="file-import" href="/account/prompt-import">
    Import discovered keywords as tracked prompts.
  </Card>

  <Card title="Content Module" icon="file-lines" href="/content/overview">
    Create content targeting researched keywords.
  </Card>

  <Card title="Site Analyzer" icon="magnifying-glass-chart" href="/tools/site-analyzer">
    Audit your pages for keyword optimization.
  </Card>

  <Card title="Prompt Strategy" icon="bullseye" href="/account/prompt-strategy">
    Build a keyword-informed prompt portfolio.
  </Card>
</CardGroup>


# Schema Optimiser
Source: https://docs.superlines.io/tools/schema-optimiser

Analyze and optimize Schema.org structured data for any webpage to improve AI search visibility.

The Schema Optimiser analyzes any URL for existing Schema.org structured data and generates AI-optimized JSON-LD markup ready to paste into your page. Structured data helps AI models understand your content and increases the likelihood of being cited in AI responses.

## Why Schema.org matters for AI search

Schema.org structured data provides explicit signals to AI models about what your page contains — your product details, article metadata, organization information, FAQ content, and more. Pages with well-implemented schema markup are easier for AI models to parse, quote, and cite.

## How to use the Schema Optimiser

<Steps>
  <Step title="Enter a URL">
    Navigate to **Schema Optimiser** in the Tools section. Enter the URL of the page you want to analyze.
  </Step>

  <Step title="Run the analysis">
    Click **Analyze Schema**. The tool detects and evaluates all structured data on the page.
  </Step>

  <Step title="Review the results">
    You'll see a Schema Quality Score, detected schema types, and a detailed breakdown of what was found.
  </Step>

  <Step title="Copy the optimized schema">
    The tool generates an improved JSON-LD version with AI-recommended changes. Copy it and add it to your page.
  </Step>
</Steps>

<Tip>
  Try the example sites (apple.com, shopify.com, bbc.com/news, stripe.com) to see how major sites implement schema and what the optimization process looks like.
</Tip>

## What the tool detects

The Schema Optimiser scans for structured data in multiple formats:

| Format | Description |
| - | - |
| **JSON-LD** | The recommended format — embedded in `<script>` tags |
| **Microdata** | Inline HTML attributes (itemscope, itemprop) |
| **Meta Tags** | Open Graph, Twitter Cards, and other meta-based markup |
| **Page Content** | Implicit data from headings, content structure, and page elements |
| **Rich Results** | Eligibility for Google rich results (FAQ, How-to, Product, etc.) |

## Understanding the quality score

The Schema Quality Score (0–100) reflects how well your structured data serves AI search visibility:

| Score | Rating | What it means |
| - | - | - |
| 80–100 | Good | Well-structured schema that supports AI discovery |
| 50–79 | Fair | Basic schema present but missing key properties |
| 0–49 | Needs Work | Minimal or no useful structured data |

The score accounts for completeness, accuracy, use of recommended properties, and alignment with AI search best practices.

## The optimization output

The tool provides:

* **Summary of changes** — how many nodes were added, enriched, or removed
* **AI-generated change list** — bullet-point explanation of each recommended change
* **Highlighted diff view** — visual comparison of your existing schema vs. the optimized version, making it easy to see exactly what changed
* **Detected schema types** — what types were found on the page (e.g., WebPage, Article, Product)
* **Page info** — title, meta description, and canonical URL of the analyzed page

## Implementing the optimized schema

You can copy the optimized JSON-LD in two formats:

1. **Raw JSON** — the JSON-LD object, useful if you manage schema through a CMS or build tool
2. **HTML script tag** — the complete `<script type="application/ld+json">` tag, ready to paste into your page's `<head>`

<Warning>
  Always review the generated schema before implementing it. While the AI optimization is designed to be accurate, verify that the content matches your page and meets your requirements.
</Warning>

## Related tools

<CardGroup>
  <Card title="AI Search Checker" icon="magnifying-glass" href="/tools/ai-search-checker">
    Full AI search readiness analysis including schema.
  </Card>

  <Card title="Site Health" icon="heart-pulse" href="/website/site-health">
    Monitor schema quality across all your pages.
  </Card>
</CardGroup>


# Site Analyzer
Source: https://docs.superlines.io/tools/site-analyzer

Run a comprehensive technical SEO audit on any webpage with actionable recommendations.

The Site Analyzer performs a full technical SEO audit on any URL, checking for issues across security, indexability, meta tags, content, links, performance, mobile readiness, and structured data. It provides a health score and prioritized recommendations to fix the most impactful issues first.

## How to use it

<Steps>
  <Step title="Enter a URL">
    Navigate to **Site Analyzer** in the Tools section. Enter the URL you want to audit.
  </Step>

  <Step title="Run the audit">
    Click **Run Audit**. The tool crawls the page and evaluates it across all categories. This takes 15–60 seconds depending on page complexity.
  </Step>

  <Step title="Review the health score">
    The overall health score (0–100) appears at the top, along with counts of critical issues, warnings, notices, and passed checks.
  </Step>

  <Step title="Work through recommendations">
    The Priority Fixes section lists the most impactful issues to address. Expand each category in the detailed analysis for the full picture.
  </Step>
</Steps>

## Health score

The overall score reflects the technical health of the page:

| Score | Rating | Meaning |
| - | - | - |
| 80–100 | Excellent | Few or no issues found |
| 60–79 | Good | Some issues to address but fundamentally sound |
| 40–59 | Needs Improvement | Significant issues that could affect AI visibility |
| 0–39 | Poor | Critical issues requiring immediate attention |

## Issues summary

Four cards at the top provide a quick overview:

| Card | What it shows |
| - | - |
| **Critical Issues** | Problems that must be fixed — blocking indexing or AI discovery |
| **Warnings** | Important issues that degrade performance or visibility |
| **Notices** | Minor suggestions for improvement |
| **Passed** | Checks that passed successfully |

Click any card to filter the detailed analysis to show only that severity level.

## Page information

A summary card shows key page metadata:

* URL and page title
* HTTPS status
* Word count
* H1 tag count
* Image count (total and with alt text)
* Internal and external link counts

## Detailed analysis by category

Issues are organized into categories, each expandable:

| Category | What it checks |
| - | - |
| **Security** | HTTPS, mixed content, security headers |
| **Indexability** | Robots meta, canonical tags, sitemap presence |
| **Meta Tags** | Title, description, Open Graph, social tags |
| **Content** | Word count, heading structure, readability, duplicate content |
| **Links** | Broken links, redirect chains, anchor text quality |
| **Performance** | Page speed indicators, resource optimization |
| **Mobile** | Mobile responsiveness, viewport configuration |
| **Structured Data** | Schema.org markup, JSON-LD validation |

Each issue within a category includes:

* **Type** — critical, warning, notice, or passed
* **Title** — what the issue is
* **Description** — why it matters
* **Affected elements** — how many elements are impacted
* **Recommendation** — how to fix it

## Priority fixes

A curated list of the highest-impact issues to fix first, ranked by severity and potential improvement. This section pulls the most critical and warning-level issues to the top so you know exactly where to start.

## Sidebar navigation

A sticky sidebar shows all issue categories with their issue counts, letting you jump directly to any category without scrolling.

## Actions

* **Re-Audit** — run the audit again on the same URL to verify fixes
* **New Audit** — clear results and start with a different URL
* **Print** — print the full report

## Related tools

<CardGroup>
  <Card title="AI Search Checker" icon="magnifying-glass" href="/tools/ai-search-checker">
    AI-specific readiness analysis complementing the technical audit.
  </Card>

  <Card title="Schema Optimiser" icon="code" href="/tools/schema-optimiser">
    Deep-dive into structured data optimization.
  </Card>

  <Card title="Site Health" icon="heart-pulse" href="/website/site-health">
    Ongoing monitoring of your entire site's health.
  </Card>

  <Card title="Keyword Research" icon="key" href="/tools/keyword-research">
    Research keywords to inform your content strategy.
  </Card>
</CardGroup>


# Crawler Analytics
Source: https://docs.superlines.io/website/crawler-analytics

Track and analyze AI bot crawler traffic and human visitors to your website.

Crawler Analytics shows how AI bots and human visitors interact with your website. It goes beyond the conceptual coverage in [AI Crawler Analytics](/metrics/ai-crawler-analytics) with a full page walkthrough of the dashboard, its tabs, and how to use the data.

## Setup

To start tracking crawler traffic, install the Superlines tracking script on your website. The Crawler Traffic tab on the [Overview Dashboard](/analytics/overview-dashboard) provides setup instructions with platform-specific guides for:

* **Standard websites** — JavaScript snippet
* **Webflow, WordPress, Shopify, Next.js** — platform-specific instructions
* **Netlify, Vercel** — Edge Functions setup

<Info>
  For detailed setup instructions, see the [AI Crawler Analytics](/metrics/ai-crawler-analytics) documentation.
</Info>

## Crawlers tab

### KPI cards

| Card | What it shows |
| - | - |
| **Top Crawler** | The most active bot visiting your site |
| **Top URL** | The page receiving the most bot traffic |
| **Top Category** | The most common bot category |
| **Top Country** | Where most bot traffic originates |

### Charts and tables

* **Bot Traffic Trend** — total bot traffic over time, or broken down by individual bot
* **Top Bots** — ranked table of bots with name, label, category, visit count, and percentage share
* **Top Crawled Pages** — which pages bots visit most, with both bot and human visit counts
* **Bot Categories** — pie chart and table showing the distribution of bot types
* **Bots by Country** — geographic distribution of crawler traffic

## Visitors tab

Human traffic analytics for comparison with bot activity:

### KPI cards

| Card | What it shows |
| - | - |
| **Visitors** | Unique visitors (by IP) |
| **Pageviews** | Total page views |
| **Top Country** | Where most human traffic originates |
| **Top Referral** | Most common referral source |

### Charts and tables

* **Traffic Trend** — visitors and pageviews over time
* **Top Pages** — most visited pages
* **Top Referrers** — where your visitors come from
* **Visitors by Country** — geographic breakdown
* **Visitors by Device** — Mobile, Tablet, Desktop distribution
* **Visitors by Browser** — Chrome, Safari, Firefox, Edge, etc.

## Human vs Bot tab

A direct comparison view showing:

* **Total Visits** — combined human and bot visits
* **Bot Visits** — absolute bot visit count
* **Human Visits** — absolute human visit count
* **Bot Percentage** and **Human Percentage** — relative shares
* **Human vs Bot Trend** — chart comparing traffic over time
* **Top Bots** — ranked list of most active bots

## AI Bot Data tab

Focused specifically on AI-related bot activity:

### Citation stats

Cards showing activity from specific AI bots:

* ChatGPT Citations
* Perplexity Bot activity
* Claude Bot activity
* Copilot activity

Each card includes a trend indicator showing whether activity is increasing or decreasing.

### AI-specific analytics

* **Top Pages** — pages most visited by AI bots
* **Citation Sources** — which AI services are citing your content
* **AI Bot Traffic Trend** — traffic split between training bots and search bots
* **AI Crawlers** — individual bot activity (OAI-SearchBot, GPTBot, ClaudeBot, PerplexityBot, etc.)
* **Traffic Split** — visualization of training vs. search bot proportions

<Tip>
  Pay special attention to the Training vs Search bot split. Search bots (like OAI-SearchBot, PerplexityBot) indicate your content is being used in real-time AI search results. Training bots (like GPTBot) indicate your content is being used for model training — both are valuable signals.
</Tip>

## Filters

Each tab has relevant filters:

* **Crawlers tab** — filter by Bot, Country, Page
* **Visitors tab** — filter by Page, Referrer, Device, Browser, Country
* **Human vs Bot tab** — filter by Bot, Country

## Exporting data

All tables support CSV and JSON export via the export dropdown.

## Related pages

<CardGroup>
  <Card title="AI Crawler Analytics" icon="robot" href="/metrics/ai-crawler-analytics">
    Conceptual guide to AI crawler tracking.
  </Card>

  <Card title="GA4 AI Traffic" icon="chart-simple" href="/website/ga4-ai-traffic">
    Track AI traffic using Google Analytics data.
  </Card>

  <Card title="Site Health" icon="heart-pulse" href="/website/site-health">
    Monitor page-level health and AI readiness.
  </Card>

  <Card title="Overview Dashboard" icon="gauge" href="/analytics/overview-dashboard">
    Crawler traffic summary in the Overview.
  </Card>
</CardGroup>


# GA4 AI Traffic
Source: https://docs.superlines.io/website/ga4-ai-traffic

Analyze traffic coming to your website from AI-powered search platforms.

The GA4 AI Traffic page shows how much traffic your website receives from AI platforms like ChatGPT, Perplexity, Copilot, Claude, and others. It connects to your Google Analytics 4 property to detect and analyze AI-referred visits.

## Setup

Before you can use this page, you need to connect your Google Analytics property:

<Steps>
  <Step title="Connect Google Analytics">
    If you haven't already, go to the [Integrations](/integrations/google-analytics) page to connect your GA4 property.
  </Step>

  <Step title="Select your property">
    On the AI Traffic page, select your Google Analytics property and optionally your Google Search Console site from the configuration section.
  </Step>

  <Step title="Click Configure Analytics">
    Save your property selection to start receiving AI traffic data.
  </Step>
</Steps>

<Info>
  AI traffic detection works by analyzing referral sources in your GA4 data. It identifies visits originating from known AI platforms and categorizes them accordingly.
</Info>

## Key metrics

Four summary cards show your AI traffic performance:

| Metric | Description |
| - | - |
| **Total Sessions** | AI-referred sessions in the selected period |
| **Total Page Views** | Page views from AI-referred traffic |
| **Total Users** | Unique users arriving from AI platforms |
| **Top LLM** | The AI platform sending the most traffic, with its share percentage |

Each metric shows the percentage change compared to the previous period.

## LLM referral traffic chart

A stacked bar chart showing AI-referred sessions over time, broken down by AI engine. This reveals:

* Which AI platforms drive the most traffic to your site
* How AI traffic volume changes over time
* Whether specific platforms are growing or declining

## Detailed tabs

### Landing Pages

Shows which pages on your site receive the most AI traffic:

| Column | Description |
| - | - |
| **Page Path** | URL path of the landing page |
| **Page Title** | The page title |
| **Sessions** | Number of AI-referred sessions |
| **Users** | Unique users |
| **Bounce Rate** | Percentage of single-page visits |
| **LLM Engines** | Which AI platforms sent traffic to this page |

### Geographic

AI traffic broken down by country:

| Column | Description |
| - | - |
| **Country** | Visitor country |
| **Sessions** | Number of sessions |
| **Users** | Unique users |
| **Bounce Rate** | Single-page visit rate |
| **Top Engine** | Most common AI referral source for this country |

### Devices

AI traffic by device type, shown as both a pie chart and table:

* Device category (Desktop, Mobile, Tablet)
* Sessions and users per device
* Bounce rate
* Top AI engine per device type

## Platform performance sidebar

A sidebar panel shows individual AI platform performance:

* Each platform listed with session count, user count, and percentage share
* Top landing page highlighted
* Quick stats: average session duration, average bounce rate, top country, top device

## Filters

* **Date range** — predefined (7 days, 30 days, 3 months, 6 months) or custom start/end dates
* **LLM engines** — filter by specific AI platforms (multi-select)

<Tip>
  Compare your AI traffic trends with your brand visibility metrics. If brand visibility is increasing but AI traffic isn't following, your content may be mentioned but not cited with links — focus on earning more citations through the [Citations Analysis](/analytics/citations-page) page.
</Tip>

## Related pages

<CardGroup>
  <Card title="Google Analytics Integration" icon="chart-simple" href="/integrations/google-analytics">
    How to connect your GA4 property.
  </Card>

  <Card title="Crawler Analytics" icon="robot" href="/website/crawler-analytics">
    Track AI bot crawling activity on your site.
  </Card>

  <Card title="Citations Analysis" icon="link" href="/analytics/citations-page">
    See which URLs are cited in AI responses.
  </Card>

  <Card title="Key Metrics" icon="chart-bar" href="/metrics/key-metrics">
    Understand citation rate and its impact on traffic.
  </Card>
</CardGroup>


# Site Health
Source: https://docs.superlines.io/website/site-health

Monitor your website's health scores and fix issues that affect AI search visibility.

Site Health tracks the technical health and AI readiness of individual pages on your website. Unlike [Crawler Analytics](/website/crawler-analytics) which tracks bot traffic, Site Health evaluates your actual page quality — technical SEO, content structure, and Answer Engine Optimization (AEO) readiness.

## Health scores

Each analyzed page receives three scores:

| Score | What it measures |
| - | - |
| **Overall** | Combined health score factoring in all dimensions |
| **Technical** | Technical SEO health — meta tags, links, performance, security |
| **AEO** | Answer Engine Optimization — how well the page is structured for AI citation |

Scores are displayed as circular gauges and tracked over time with trend charts. The aggregate scores across all your monitored pages are shown at the top of the page.

## URL table

The main table lists all pages being monitored:

| Column | Description |
| - | - |
| **URL** | The page URL |
| **Page Title** | The page's title tag |
| **Status** | Processing state (discovered, queued, processing, complete, error) |
| **Last Analyzed** | When the page was last crawled and analyzed |
| **Overall Score** | Combined health score |
| **Technical Score** | Technical SEO score |
| **AEO Score** | Answer Engine Optimization score |
| **Issues** | Number of issues detected |

## Adding pages

You can add pages for monitoring in three ways:

<Tabs>
  <Tab title="Single URL">
    Enter an individual URL to add a specific page for monitoring.
  </Tab>

  <Tab title="Sitemap">
    Provide your sitemap URL to automatically discover and add all pages listed in it.
  </Tab>

  <Tab title="Auto-discover">
    Let Superlines crawl your domain to discover pages automatically.
  </Tab>
</Tabs>

Use the **Sync Pages** button to re-discover URLs from your domain at any time.

## Page detail view

Click any URL in the table to open the detailed health report:

* **Page metadata** — URL, title, meta description, last audit date
* **Health scores** — Overall, Technical, and AEO with visual gauges
* **Issues list** — each issue includes:
  * Title and description
  * Severity level (critical, warning, notice)
  * Category (security, meta tags, content, links, etc.)
  * Recommended fix
* **Comprehensive report** — full Markdown analysis of the page
* **Content analysis** — detailed content quality evaluation
* **Schema optimization** — structured data recommendations
* **Code snippets** — ready-to-implement fixes
* **Suggested prompt** — a pre-built prompt you can copy and use with the [AI Agent](/features/agent) for deeper analysis

### Actions in detail view

* **Re-analyze** — trigger a fresh analysis of the page
* **Mark issues as fixed** — track your remediation progress
* **Copy Agent prompt** — send the page context to the AI Agent for guidance

## Usage and quotas

The page header shows your current usage:

* Plan name and analysis interval
* Number of domains and organizations
* Monthly analyses used vs. limit

<Warning>
  Site health analyses count against your monthly quota. Check your usage regularly and prioritize analyzing your most important pages first.
</Warning>

## Site health trends

A trend chart shows how your aggregate site health scores change over time. Use the date range selector to view trends over 30, 60, or 90 days. This helps you track whether your optimization efforts are improving overall site health.

## Related pages

<CardGroup>
  <Card title="AI Search Checker" icon="magnifying-glass" href="/tools/ai-search-checker">
    One-off analysis of any URL for AI readiness.
  </Card>

  <Card title="Site Analyzer" icon="magnifying-glass-chart" href="/tools/site-analyzer">
    Full technical SEO audit of any page.
  </Card>

  <Card title="Schema Optimiser" icon="code" href="/tools/schema-optimiser">
    Optimize structured data for monitored pages.
  </Card>

  <Card title="AI Agent" icon="robot" href="/features/agent">
    Ask the Agent for guidance on fixing issues.
  </Card>
</CardGroup>