The GEO data layer for your AI agent
One MCP connection to managed AI scrapers. Your agent fetches what ChatGPT, Perplexity and Gemini actually answer — raw text, citations, sources — and runs the generative engine optimization analysis itself.
chatsights.fetch({
query: "best crm for early-stage startups",
surfaces: [
"chatgpt",
"perplexity",
"gemini"
],
country: "US"
})Answer text returned by the surface, preserved without a ChatSights score or conclusion.
Answer text returned by the surface, preserved without a ChatSights score or conclusion.
Answer text returned by the surface, preserved without a ChatSights score or conclusion.
The AI surfaces your buyers ask — scraped upstream, delivered as one schema
- ChatGPT
- Perplexity
- Google Gemini
- Google AI Overview
- Google AI Mode
- Microsoft Copilot
GEO, split the right way
Your agent owns the judgment. ChatSights owns the plumbing. The boundary is explicit, so you always know which layer produced what.
Your agent asks
One MCP tool call with a prompt, the surfaces to check and a locale.
ChatSights fetches
We run the managed scraper jobs, handle credentials, normalize records and meter usage.
Your agent analyzes
Rank, sentiment and share of voice are computed inside your agent, with your methodology.
GEO work your agent can start today
ChatSights delivers the raw records. Your agent — and your prompts — turn them into answers.
Brand visibility tracking
Schedule the prompts your buyers actually ask. Your agent diffs each run and decides whether your brand's presence really changed.
Citation and source audits
Every record carries its sources. Your agent checks which pages the AI surfaces cite — and which of yours never appear.
Competitor watch
Fetch the same prompt across six surfaces. Your agent compares who gets named, quoted and linked.
Content-gap discovery
Your agent mines raw answers for the questions your docs never answer, then files the fixes in your own workflow.
A data plane, not an analyst
GEO dashboards sell you their conclusions. ChatSights hands your agent the evidence instead — it never invents a rank, sentiment score or recommendation, so your methodology stays yours and every claim stays auditable.
answer_textprovider recordcitations[]provider recordprovider_record_idChatSights enveloperank / sentiment / adviceYour local agentStop teaching agents six APIs
Dataset IDs, request modes and provider quirks stay behind one stable contract, so the GEO workflows you build today don't break when a surface changes tomorrow.
- One normalized record shape across every surface.
- Credits only for records successfully delivered.
- Provider secrets remain on the server.
chatsights.fetch({
query: "best crm for early-stage startups",
surfaces: ["chatgpt", "perplexity", "gemini"],
country: "US",
language: "en"
})Every surface your buyers ask
Six AI surfaces through one managed AI-scraper connection, available from your local development environment.
6 AI surfaces through one managed AI-scraper connection. One credit per successfully delivered provider record.
Schedule collection, not conclusions
Run your GEO prompt set daily and receive job-status webhooks. Your agent reads the fresh records and decides whether anything changed enough to matter.
Open schedulesWhere ChatSights fits
Agent-native GEO needs raw data underneath. Other jobs are better served by other tools.
Direct provider API
Best when your team wants full provider control and will own dataset IDs, polling and schema mapping itself.
DIY browser automation
Best when you need custom interaction logic and are ready to maintain browsers, proxies and parsers.
GEO analytics dashboards
Best when humans want finished scores and reports instead of raw records an agent can reason over.
Put GEO inside the agent you already use
Connect once. Fetch what the AI surfaces actually answer about your brand. Let your agent decide what it means.