Domains and tracking
Domain
Domain
A domain is a website or brand that you track inside Rankahead. It is the central unit of organization — prompts, competitors, and analytics are all scoped to a domain.When you create a domain you provide three things:
- Display name — a human-readable label used throughout the interface.
- Website URL — the canonical URL of the brand’s site, used to identify citations in AI responses.
- Brand name — the exact string Rankahead looks for inside LLM-generated answers to detect a mention.
Competitor
Competitor
A competitor is another brand that you add to a domain for benchmarking purposes. When Rankahead analyzes AI responses, it looks for both your brand and all configured competitors. This data feeds into share-of-voice calculations and answer gap analysis.You can add competitors during onboarding or at any time from Settings → Domains → [your domain] → Competitors. Each competitor requires a brand name and website URL.
Prompts and runs
Prompt
Prompt
A prompt is a query that Rankahead sends to one or more LLM providers to check whether your brand appears in the answer. Prompts represent the questions your potential customers might type into ChatGPT, Perplexity, Google AI Overviews, or Anthropic.A prompt has four key fields:
Prompts are managed from Dashboard → Prompts. You can also trigger a prompt manually at any time with the Run now action.
| Field | Purpose |
|---|---|
| Name | Internal label for identifying the prompt in the dashboard |
| Query text | The exact question submitted to the LLMs |
| Brand name | The string to look for in responses (inherited from the domain) |
| Schedule | A cron expression or preset interval that controls when the prompt runs automatically |
Prompt run
Prompt run
A prompt run is a single execution of a prompt. When a run starts, Rankahead sends the query text to every configured LLM provider simultaneously — OpenAI, Anthropic, Google, and Perplexity — and records each response. After collecting all responses, it extracts mentions, citations, and competitor appearances.Runs can be triggered in two ways:
- Scheduled — automatically on the cron schedule you configure for the prompt.
- Manual — on demand by clicking Run now in the interface or calling the API.
Visibility metrics
Visibility score
Visibility score
The visibility score is a 0–100 number that summarizes how consistently your brand appears in AI responses across all prompt runs for a domain. A score of 100 means your brand was mentioned in every response from every LLM for every prompt. A score of 0 means your brand did not appear in any response.The score is calculated from a weighted combination of mention rate and response position (brands mentioned earlier in a response score higher). It is designed to give you a single number you can track over time and compare across domains or competitors.
Visibility scores are provider-specific as well as aggregate. You can filter the dashboard to see your score broken down by ChatGPT, Perplexity, Google AI Overviews, or Anthropic separately.
Mention rate
Mention rate
The mention rate is the percentage of prompt runs in which your brand was mentioned at least once by a given LLM provider. For example, if you ran a prompt 10 times and ChatGPT mentioned your brand in 7 of those runs, your mention rate for that prompt on ChatGPT is 70%.Mention rate gives a raw measure of how reliably a specific LLM includes your brand when answering a particular type of question.
Share of voice
Share of voice
Gap analysis
Answer gap
Answer gap
An answer gap is a query or topic where one or more competitors appear in AI-generated answers but your brand does not. Answer gaps represent missed opportunities — questions that real users are asking AI tools where your brand is invisible.Rankahead identifies answer gaps automatically by comparing mention data across your brand and all configured competitors for every prompt. You can review them in Dashboard → Gap Analysis, sorted by competitive exposure and potential impact.Closing an answer gap typically means creating or improving content so that LLMs learn to associate your brand with that topic. GEO tasks (see below) help you track the actions required to close each gap.
Citation
Citation
A citation is a domain URL that an LLM references or links in its response — either as a source, a recommendation, or a suggested next step. Citations matter because LLMs learn from the content they index; being cited frequently signals topical authority.Rankahead extracts citations from every prompt run response and tracks which domains are cited most often in answers related to your prompts. You can view citation data in Dashboard → Citations. Building citations — getting your domain referenced in high-quality content that LLMs surface — is one of the primary GEO optimization strategies.
Optimization
GEO task
GEO task
A GEO task is a specific optimization action tracked on a kanban board in the Content → GEO Tasks section. Tasks represent the concrete steps you take to improve your AI visibility — such as writing a blog post targeting an answer gap, adding schema markup, or earning citations from authoritative sources.Each GEO task has a category that describes the type of optimization:
Tasks move through kanban columns (for example, To do → In progress → Done) so you can track the status of your GEO program at a glance.
| Category | Description |
|---|---|
CONTENT_GAP | Create or improve content to target a query where competitors appear but you do not |
SCHEMA_MARKUP | Add structured data markup to help LLMs extract and surface information about your brand |
CITATION_BUILDING | Get your domain referenced in content that LLMs are likely to surface in relevant answers |
FORUM_PRESENCE | Participate in forums, communities, and Q&A sites where LLMs draw answers from |
Integrations
Google Search Console integration
Google Search Console integration
Rankahead can connect to your Google Search Console account to import traditional search performance data alongside your AI visibility metrics. This lets you identify striking-distance keywords — queries where your pages rank on page 2 or 3 of Google — and correlate traditional search rankings with AI visibility trends.You connect Google Search Console from Settings → Integrations. Once connected, the data appears in Dashboard → Search Performance.
CMS integration
CMS integration
A CMS integration connects Rankahead to your content management system so you can publish AI-generated blog posts directly from the Content section without copying and pasting. Rankahead pushes the generated post — including title, body, and metadata — to your CMS with one click.You configure CMS integrations from Settings → Integrations. See CMS integrations for setup instructions.
MCP token
MCP token
An MCP token is a Bearer authentication token that you use to connect an AI agent to Rankahead’s Model Context Protocol (MCP) server. The MCP server exposes Rankahead’s data and actions as structured tools that any MCP-compatible AI agent — such as Claude, Cursor, or a custom agent — can call programmatically.The MCP server endpoint is You generate and manage MCP tokens from Settings → API Keys. See MCP integration for the full list of available tools and example workflows.
POST https://app.rankahead.ai/api/mcp. You authenticate by including your token in the Authorization header:Next steps
Quickstart
Follow the step-by-step guide to create your first domain, prompt, and run.
Dashboard overview
See how visibility scores, mention rate, and share of voice appear in the analytics dashboard.
GEO tasks
Learn how to create, categorize, and manage GEO tasks to track your optimization work.
MCP integration
Connect an AI agent to Rankahead for automated visibility monitoring and reporting.