The Peec AI alternative for developers

AgentGEO is a Peec AI alternative built for developers. Peec AI is an AI-search analytics dashboard: it tracks prompts, scores your brand's visibility, and benchmarks you against competitors across AI engines. AgentGEO sits one layer down. One API - or one MCP server - returns the raw answers ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini actually give: the answer text, the citations, the sources, and provider metadata (surfaceKey, providerFields). Your agent computes the metrics. You own the data.

Let's be clear up front: Peec AI is a genuinely strong product for its audience. If your marketing team wants prompt tracking and brand-visibility scores in a polished UI, it delivers exactly that. AgentGEO serves the other buyer - the developer or agency who wants the answers themselves, in JSON, inside their own code. Not a dashboard to log into; a data layer to build on.

The best Peec AI alternative for builders

The difference is not a feature checklist - it is which layer of the stack you buy. Peec AI sells the finished analysis: scores, benchmarks, trend lines. AgentGEO sells the substrate those things are computed from: the answers and citations themselves. If you are embedding AI visibility into your own SaaS, building an internal GEO pipeline, or white-labeling reports as an agency, the substrate is what you need. Four reasons developers make that trade:

Own the raw answers

Every fetch returns clean JSON: an answers[] array with each engine's answerText, the sources[] it cited (title, URL, position), and metadata telling you exactly which surfaceKey and providerFields produced it. That JSON lands in your database, not a vendor's. Join it against your CRM, replay it through a new scoring model next quarter, export it, delete it. An analytics dashboard shows you conclusions; the answer-access layer gives you evidence.

Build it into your product

Analytics dashboards are terminal nodes - data goes in, charts come out, and that is where it ends. An API is a component. AgentGEO's output goes wherever your code goes: a visibility widget in your customer console, a nightly job feeding your warehouse, a white-labeled agency report with your logo on it. If AI-visibility data is a feature of your product rather than a report you read, you need the API shape.

Usage pricing, not seats

AgentGEO bills on usage - run-based plans, with a spend cap - because the caller is usually a script, not a person. A cron job fetching six engines nightly is metered by the calls it makes; nobody buys a login for a machine. Pricing scales with call volume rather than headcount.

MCP-native by design

AgentGEO ships as an MCP server (agentgeo-mcp on npm), so Claude Code, Cursor, Codex, or any MCP client can pull live answers mid-task. Ask your agent "who does Perplexity cite for our category this week?" and it fetches the raw data and reasons over it in context - no switching into a separate analytics tab. A dashboard cannot be a tool inside your agent; an MCP server is exactly that.

Peec AI vs AgentGEO at a glance

A fair comparison - these are different products for different buyers, not two versions of the same thing. Find your row.

Their metrics vs your metrics

A visibility score from any packaged dashboard is a black box. It is some weighting of mentions, positions, and prompts - computed the vendor's way, on the vendor's cadence. You can read the number, but you cannot audit the formula, and you cannot bend it to how your business defines winning.

The raw answer layer inverts that. AgentGEO hands you every answer and every citation, so your share-of-voice definition becomes a function in your codebase. Count only first-position citations. Weight ChatGPT double because that is where your buyers are. Track a competitor's domain across all six engines. When the definition is code you own, you can audit it, version it, and change it the day your strategy changes - and the historical raw data is still there to recompute against. See what a GEO data layer is for the fuller argument.

Everything a metric is made of

Every visibility score, share-of-voice chart, and competitor benchmark decomposes into the same primitives. AgentGEO returns them all:

• Six engines, one interface: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini - surface keys chatgpt, perplexity, gemini, google_ai_overview, copilot.
• The actual answer text each engine gave, not a proxy for it.
• Citations and sources with title, URL, and position - the atoms of any share-of-voice number.
• Provider metadata (surfaceKey, providerFields) so every data point is attributable.
• Managed-scraper engine, maintained for you - no headless browsers, proxies, or CAPTCHA fights behind the data.

One call to the raw answers

Here is the entire integration surface - one authenticated POST. Add engines by extending the surfaces array.

curl -X POST https://api.agentgeo.org/v1/fetches \
  -H "Authorization: Bearer ag_live_..." \
  -H "Content-Type: application/json" \
  -d '{"query": "best crm for startups", "surfaces": ["chatgpt"]}'

# → {
#     "answers": [
#       { "surfaceKey": "chatgpt", "answerText": "...",
#         "sources": [{ "title": "...", "url": "https://...", "position": 1 }] }
#     ]
#   }

Working in Python or Node? The Python guide and Node guide walk through the same call with error handling and multi-engine fan-out.

Start computing your own metrics

If a ready-made visibility score in a polished dashboard is what your team needs, Peec AI is a strong choice and we will say so plainly. If you want the answers underneath the score - to define the metric yourself, audit it, and ship it inside your own product - AgentGEO is the layer built for that.