Learn how AmICited tracks your brand across ChatGPT, Perplexity, Gemini, Google AI Overviews, AI Mode and more, what every metric means, and how to run it all from your AI agents.
AmICited asks AI engines the questions your buyers ask, stores every answer, and records which brands each answer names and which pages it cites. From that record it calculates your visibility, your share of voice against competitors, and the sources that decide who gets recommended.
These docs explain how the data is collected, what each number means and how it is calculated, and how to act on it: in the dashboard, or from Claude, ChatGPT, Cursor or Codex through the MCP server.
You track a domain. For that domain you write prompts: the questions a buyer would type into an AI assistant. On a schedule you choose, AmICited sends each prompt to each selected AI engine from the country you picked, and stores the response. Every response is parsed for brand mentions (brands named in the answer, in order) and citations (the URLs the engine used as sources). Your metrics are aggregates of those two records over the date range and filters you choose. The terminology page defines every term in one place.
Prefer to ask your agent?
Everything in the dashboard is also available as an MCP tool. Once connected, you can ask “which prompts cite my competitors but not us this month?” and your agent pulls the answer from the same data.
# AmICited documentation
Learn how AmICited tracks your brand across ChatGPT, Perplexity, Gemini, Google AI Overviews, AI Mode and more, what every metric means, and how to run it all from your AI agents.
AmICited asks AI engines the questions your buyers ask, stores every answer, and records which brands each answer names and which pages it cites. From that record it calculates your visibility, your share of voice against competitors, and the sources that decide who gets recommended.
These docs explain how the data is collected, what each number means and how it is calculated, and how to act on it: in the dashboard, or from Claude, ChatGPT, Cursor or Codex through the [MCP server](/docs/mcp/connect/).
## Start here
Add your domain, pick prompts and engines, and get your first AI answers in about ten minutes.What happens on every run: which engines are asked, from where, and what gets stored.Visibility score, share of voice, average rank and citations, with the exact formulas.Give Claude, ChatGPT, Cursor or Codex live access to your AmICited data.
## Browse by topic
Domains, prompts, engines, countries, cadence, competitors and tags.The dashboard, prompt detail, sources, fan-out and citation gaps.Articles, annotations, domain audit, freshness and Web Vitals.Search Console, Bing, GA4, ad platforms and ecommerce stores.Plans, limits, credits and team roles.Zero visibility, sudden drops, missing engines, uncited pages.
## The model in one paragraph
You track a **domain**. For that domain you write **prompts**: the questions a buyer would type into an AI assistant. On a **schedule** you choose, AmICited sends each prompt to each selected **AI engine** from the **country** you picked, and stores the **response**. Every response is parsed for **brand mentions** (brands named in the answer, in order) and **citations** (the URLs the engine used as sources). Your metrics are aggregates of those two records over the date range and filters you choose. The [terminology](/docs/terminology/) page defines every term in one place.
Everything in the dashboard is also available as an MCP tool. Once [connected](/docs/mcp/connect/), you can ask "which prompts cite my competitors but not us this month?" and your agent pulls the answer from the same data.
Source: https://www.amicited.com/docs/
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