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Agent Analytics

See how AI agents perceive your brand — across every surface

Track mention rate, citation share, and sentiment across 7 major LLM surfaces — ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews, and Microsoft Copilot — re-checked daily, with full citation source leaderboards and share-of-voice.

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What “agent analytics” means

Agentic search is the new SEO. People ask LLMs “best dentist in [city]”, “which CRM integrates with HubSpot for solo founders”, “is [your-brand] any good” — and the model answers with a recommendation drawn from across the web. The buyer never sees a results page. They see one answer, and you’re either named in it or you’re not.

These models cite sources differently than Google. Domain authority alone isn’t enough; what matters is how often each model surfaces you when a buyer asks, what sentiment the mention carries, what rank position you sit in within the answer, and which third-party domains the model trusts as the citation source for that prompt. Those are different metrics from organic position, and they require a different measurement stack.

rank.ai tracks all 7 major answer-engine surfaces and gives you the patterns to act on — daily re-checks, persistent history, sentiment classification, citation leaderboards, and share-of-voice across competitors. Below is the surface matrix, the capability set, the honest comparison against single-shot checks and scraping-based tools, and the FAQ.

Surfaces we track

Seven AI answer surfaces. Six in production, one in stub mode pending Azure Foundry provisioning.

Microsoft Copilot is in stub mode today (P-17). Production path requires Azure Foundry + Grounding-with-Bing-Search; UI + cache + dispatcher are wired so flipping the run mode will light it up across the platform.
SurfaceProviderStatusCitationsNotes
ChatGPT
OpenAI's web-grounded gpt-4o family. Default-on for every tracked prompt.
OpenAITrackedDefault
Claude
Anthropic's claude-haiku-4-5 with web search. Default-on for every tracked prompt.
AnthropicTrackedDefault
Gemini
Google's gemini-2.5-flash with grounded search. Default-on for every tracked prompt.
GoogleTrackedDefault
Perplexity
Perplexity Sonar with native citation list. Opt-in per prompt (P-10).
PerplexityTrackedOpt-in
Grok
xAI Grok with X-grounded search. Opt-in per prompt (P-11).
xAITrackedOpt-in
Google AI Overviews
The AI block above the Google SERP. Reuses our Google SERP cache so it's cheap to add to existing prompts (P-12).
GoogleTrackedReuses SERP cache
Microsoft Copilot
Stub today (P-17). Production path requires Azure Foundry + Grounding-with-Bing — paid subscription, AAD auth, and project wiring. UI + cache + dispatcher are wired so flipping _RUN_MODE will light it up across the platform.
MicrosoftStub—Awaiting Azure provisioning

Three product surfaces, daily

Every tracked prompt feeds these three views. They update once a day across every enabled provider.

Per-prompt rank history

A graph per tracked prompt, per provider — mention rate and rank position over weeks. When you publish new content, get a new backlink, or earn a citation in a publication the LLMs trust, you can finally see whether the AI surface noticed.

Citation source leaderboard

A ranked list of every domain the LLMs cite when answering your tracked prompts, aggregated across all 7 surfaces. The domains that keep showing up are the publications you should be earning coverage in next — that’s where the model picks up the information it’s grounding answers against.

Brand mention sentiment + share-of-voice

Each mention classified as neutral, positive, or competitor-comparative. Roll those up and you get share-of-voice — the percentage of all brand mentions in your category that go to you versus each named competitor, per provider, per prompt.

What you get vs the alternatives

Single-shot free checks (including our own) are useful first reads. Scraping-based tools were the old way. Persistent multi-provider tracking is what you actually optimise against.

Capabilityrank.ai paidFree single-shot checkScraping-based tools
AI surfaces tracked
Number of distinct LLMs and answer surfaces covered, per tracked prompt.
7 (Copilot stub)3 (single-shot)1–4 (varies)
Direct provider APIs
Calls the real OpenAI / Anthropic / Google / Perplexity / xAI APIs the same way any other API customer would. No scraping, no headless browsers, no screenshot OCR.
—
Web-search grounding on by default
Each provider runs in its grounded / search-enabled mode so the model can pull live citations, not just training-data recall.
Varies
Persistent run history
Every run of every prompt against every provider is stored. The line you optimise against is rate-based, not single-snapshot.
—Varies
Citation source leaderboard
Aggregate every domain the LLMs cite across your tracked prompts so you know which publications to earn coverage in next.
——
Brand mention sentiment + share-of-voice
Each mention classified neutral / positive / competitor-comparative. Share-of-voice rolls those up across your category.
——
Daily re-checks per prompt
Daily cron re-runs each tracked prompt against every enabled provider so the visibility line averages out per-call randomness.
—Manual
Agent intent simulation
Replay the full chained tool-use trace an autonomous agent would generate for a buying-intent task. NOT shipped today on any side — be honest.
———

Who uses this

Three buyer profiles where AI visibility is already a budget line, not a curiosity.

SEO teams

Track how AI assistants surface your domain — alongside your existing Google rank tracking stack. Mention rate per prompt, citation source leaderboard, and share-of-voice tell you where to invest content and PR effort to move the AI-search needle.

Agencies

White-label client reports for AI visibility, plus an embeddable widget at /embed for live dashboards inside client portals. See /pricing#agency for the white-label tier.

Brand teams

Sentiment tracking and citation monitoring for crisis response and reputation work. Catch a competitor-comparative shift in how an LLM frames your category before it shows up in inbound conversations.

Frequently asked.

Which AI assistants do you track?
Seven surfaces today: ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, Grok (xAI), Google AI Overviews, and Microsoft Copilot. ChatGPT, Claude, and Gemini are default-on for every tracked prompt. Perplexity, Grok, and Google AI Overviews are opt-in per prompt. Microsoft Copilot is in stub mode today — see the dedicated FAQ on Copilot below.
How is this different from regular SEO?
Regular SEO optimises a page for ranking on a Google results list. Agent analytics optimises a brand for being mentioned, cited, and recommended inside the answer an AI assistant gives back to its user. The signals matter differently: domain authority and exact-match keywords matter less than the breadth and structure of citation-worthy content the LLM can ground its answer in. The unit of measurement is also different — instead of a single position, you get a mention rate per prompt across every provider, plus a share-of-voice slice across competitors.
Why does Microsoft Copilot show 'stub'?
Microsoft does not expose the consumer Copilot (Bing Chat) experience as a public API. The closest production-grade equivalent is Azure Foundry's Bing Grounding tool, accessed through the OpenAI Responses-API surface. To wire that up we need a paid Azure subscription, an Azure AI Foundry project, a Grounding-with-Bing-Search resource in the same resource group, a project-level connection between them, plus Microsoft Entra ID (AAD) auth to mint the bearer token. The UI, cache key, dispatcher, and provider module are all in place — flipping the run mode to production is one config change once the Azure side is provisioned. Otterly and a few other tools claim Copilot in their marketing; the difference between that claim and our 'stub' label is whether the upstream API call is real. We'd rather under-promise than over-promise.
Do you support DeepSeek or Meta AI?
Both are in the roadmap, no firm timeline. We add a provider when (a) it has a stable API surface that exposes the underlying search queries the agent issues — which is the most useful signal we capture — and (b) enough buying-intent prompts in our customers' categories actually surface in it to be worth the recurring per-call cost. Neither is over the bar today. If either becomes meaningful share for your category, tell us and we'll prioritise.
How do I share results with my client?
Two paths. First, the embeddable AI-visibility widget at /embed lets you drop a live snapshot of your AI rank into any page — useful for client portals and prospect dashboards. Second, agency tiers ship white-label PDF reports with your branding and per-client workspaces (see the /pricing#agency section). Multi-tenant agency hierarchy is on the roadmap (P-15) for sub-account isolation; today, agencies typically use a workspace-per-client pattern.
What is Answer Engine Optimization?
Answer Engine Optimization (AEO) is the practice of getting your brand surfaced inside the answer an AI assistant gives back, instead of (or alongside) ranking on a Google results page. The optimisations look different from classic SEO: citations matter more than backlinks, structured factual claims matter more than keyword density, and the question you're optimising for is whatever a buyer would type into ChatGPT, not what they'd type into Google. Agent analytics is the measurement layer for AEO — you can't optimise what you can't see.
How frequently do you re-check?
Daily by default. Each tracked prompt is re-run against every enabled provider on a daily cron so the visibility line you optimise against is a rate over many runs, not a single snapshot. Daily cadence is configurable per prompt — for high-velocity launches you can pin a prompt to hourly; for stable evergreen prompts, weekly is enough. We do not over-poll: re-running the same prompt every five minutes burns provider quota without improving the trend signal.
What about prompt drift?
Each tracked prompt uses a deterministic prompt template per provider — same instructions, same temperature settings, same system message — so the variation you see in the result is from the LLM, not from us silently tweaking the question. We persist the prompt text on each run record so you can audit exactly what was sent. If you edit a prompt's wording mid-stream, we treat the edited version as a new prompt rather than re-baselining the old one, so historic comparisons stay clean.

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