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 — a subscription plus overage, 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. There is a free tier with no credit card, and 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.