The Otterly.AI alternative for developers

AgentGEO is an Otterly.AI alternative built for developers. Otterly.AI monitors prompts across ChatGPT, Google AI Overviews, and Perplexity and reports brand mentions and link visibility to marketing and SEO teams. AgentGEO exposes the layer that kind of tool is built on: POST /v1/fetches returns the raw answer any of six engines — ChatGPT, Perplexity, Google AI Overviews, Copilot, Gemini — gives for a query, with full citations, sources, and provider metadata. Your code does the watching. You keep the history.

Otterly.AI is a genuinely strong product for its audience. If you want AI-search monitoring that works on day one with zero engineering, it is a sensible buy. AgentGEO is aimed at the team that keeps hitting the edges of any packaged monitor — custom prompts, custom cadence, custom alerting — and would rather own the pipeline than configure someone else's.

The best Otterly.AI alternative for builders

A monitoring product and a data layer answer different questions. The monitor answers "is my brand mentioned?" on its schedule, in its report format. The data layer answers "what did the engine say, verbatim, with sources?" — and leaves schedule, storage, and alerting to you. That trade is the whole comparison. Here is what it buys you:

Your data, in your database

Each fetch returns JSON — answerText, a sources[] array with title, URL, and position, plus the surfaceKey and providerFields that produced it. Append tonight's run to yesterday's in your own store and you have a longitudinal dataset no vendor controls: diffable, queryable, joinable against traffic and revenue tables. Cancel a monitoring subscription and the history usually goes with it; own the fetch layer and the history is just rows in your database.

Embed anywhere your code runs

Monitoring reports are read by humans. Raw answers are consumed by software — a #brand-alerts Slack webhook, a warehouse table behind your BI tool, a client-facing widget in an agency portal. Because AgentGEO is an API plus an MCP server rather than a UI, the same data feeds all of them without exports, iframes, or copy-paste.

Metered by calls, not logins

A monitoring workload is a machine making requests on a schedule — seats are the wrong unit for that. AgentGEO bills on usage: a subscription plus overage, with a spend cap so a runaway cron cannot surprise you. The free tier needs no credit card, and pricing scales with fetch volume, not team size.

Works inside your agent (MCP)

Connect the MCP server — claude mcp add agentgeo -- npx -y agentgeo-mcp --key ag_live_... — and monitoring stops being a separate product at all. Claude Code, Cursor, Codex, or any MCP client can fetch live answers mid-conversation: "pull this week's answers for our top ten queries and tell me which citations changed." The analysis happens in the agent you already work in.

Otterly.AI vs AgentGEO at a glance

Neither column wins in the abstract — they are different layers of the same problem. Pick the row that sounds like your team.

Monitoring is a cron job when you own the data

Strip monitoring to its mechanics and it is four steps: fetch answers on a schedule, store them, diff each run against the last, alert when something changes. A vendor sells you those four steps pre-assembled — on their prompt list, their cadence, their report template. With the raw answer layer, the same four steps are an afternoon of code you control completely.

Schedule POST /v1/fetches from cron, GitHub Actions, or your job runner — hourly for money queries, weekly for the long tail. Store each JSON response keyed by query and date. Diff sources[] run-over-run: a competitor entering position 1, your docs dropping out of Perplexity's citations, a new domain the engines suddenly trust. Then alert wherever your team actually looks — Slack, your database, a dashboard you already have, or straight into your agent for a written summary. The vendor's report arrives on the vendor's schedule; your pipeline runs on yours.

Everything a monitor needs from the fetch layer

The monitoring loop is only as good as the data it fetches. Every AgentGEO response carries the full picture:

• Six engines from one call: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini — selected per request via the surfaces array.
• Verbatim answer text, so diffs catch wording shifts, not just mention counts.
• Sources with positions — the exact URLs cited and where they rank, the raw material of link-visibility tracking.
• `surfaceKey` and `providerFields` on every answer, so a change in results is attributable to a model change.
• Managed-scraper engine, maintained for you — a scheduled scraper breaks weekly; a scheduled API call does not.

Your first scheduled fetch

This is the entire integration — one authenticated POST your scheduler runs. Swap or extend surfaces to cover more engines.

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

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

The Python guide and Node guide show the full pattern — multi-engine fan-out, storing runs, and diffing citations over time.

Start running your own monitor

If a packaged report is enough, Otterly.AI does that well and you should use it. If monitoring belongs in your own stack — your prompts, your schedule, your database, your alerts — AgentGEO is the answer-access layer to build it on.