The Profound alternative for developers

AgentGEO is a Profound alternative built for developers. Instead of logging into a per-seat marketing dashboard, you call one API — or connect one MCP server — and get back the raw answers ChatGPT, Perplexity, Google AI Overviews, Copilot, and Gemini actually return: the answer text, the citations, the sources, and the provider metadata. Your own agent does the analysis. You own the data.

Profound is a strong product. If you are a marketing team that wants a polished, ready-made AI-visibility dashboard with reports and alerts, it is a genuinely good tool. AgentGEO is a different shape: a thin, dev-first answer-access layer you build *on*, not a closed analytics UI you log *into*. This page is for builders who want the raw data in their own product.

The best Profound alternative for builders

The core difference is not feature-by-feature — it is *ownership and shape*. Profound gives a marketing team a dashboard. AgentGEO gives an engineer an endpoint. If you are embedding AI visibility as a feature in your own SaaS, or an agency white-labeling it under your own brand, the dashboard is the thing you have to work around; the API is the thing you build with. The four cards below are the reasons developers pick the answer-access layer over a closed dashboard.

Own your data

AgentGEO returns the raw JSON — an answers[] array with each engine's answerText and the sources[] it cited, plus the surfaces you asked for and provider metadata — and it is yours. Store it, join it against your own tables, run your own diffs over time, export it, delete it. A closed dashboard owns the data model and shows you its interpretation of it; you see the charts it decided to draw. With the raw answer-access layer, the interpretation is yours to build. As we put it: AgentGEO is the answer-access layer; the dashboards are what you build on it.

Embed in your product

Per-seat dashboards assume a human logs in. That model breaks the moment you want AI-visibility data *inside your own product* — a widget in your customer's console, a column in your reporting tool, an agency deliverable under your own brand. Because AgentGEO is an API plus MCP connection, the output goes wherever your code goes. No iframe, no screen-scraping someone else's UI, no exporting CSVs by hand.

Pay by usage, not per seat

Seat-based pricing punishes the exact thing developers do: automate. If a script queries fifty engines every night, seats are the wrong meter. AgentGEO bills on usage — a subscription plus overage — so a machine making thousands of calls costs what those calls cost, and a five-person team is not paying for empty seats. You provision an API key, you watch a spend cap, you scale calls up and down with your workload.

MCP-native

This is the part a dashboard structurally cannot do. AgentGEO ships as an MCP server, so your *own* agent — Claude Code, Cursor, Codex, or any MCP client — can pull live answer data mid-task and reason over it. Ask your agent "which sources does Perplexity cite for our category, and where are we missing?" and it fetches the raw answers and analyzes them in context. The GEO/AEO analysis runs in the agent you already use, not in a separate tab you have to check.

Profound vs AgentGEO at a glance

A fair side-by-side. Neither column is "better" in the abstract — they are built for different jobs. Pick the row that describes you.

Everything you need to measure AI visibility

Switching to a raw API does not mean giving up coverage. AgentGEO runs against the engines that matter and returns the primitives a visibility tool is built from:

• Multi-engine: ChatGPT, Perplexity, Google AI Overviews, Copilot, and Gemini from one interface.
• Real answer text — the actual response the engine gave, not a proxy metric.
• Citations and sources — the URLs each engine cited, so you can see who gets referenced for a query.
• Provider metadata — engine and model on every response, so you know exactly what produced the answer.
• Zero-scrape, official-API engine — no headless-browser farms, no parsing Perplexity HTML, no bans.
• One base URL: https://api.agentgeo.org with Authorization: Bearer ag_live_....

Use the language you love

The whole switch is one authenticated POST. Here is the same call — "what does ChatGPT answer for *best running shoes*?" — in curl, Python, and Node. Add more engines to the surfaces array: perplexity, gemini, google_ai_overview, copilot.

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

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

import requests

resp = requests.post(
    "https://api.agentgeo.org/v1/fetches",
    json={"query": "best running shoes", "surfaces": ["chatgpt"]},
    headers={"Authorization": "Bearer ag_live_..."},
)
run = resp.json()

for answer in run["answers"]:
    print(answer["answerText"])
    for src in answer["sources"]:
        print(src["url"])

const resp = await fetch("https://api.agentgeo.org/v1/fetches", {
  method: "POST",
  headers: {
    Authorization: "Bearer ag_live_...",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ query: "best running shoes", surfaces: ["chatgpt"] }),
});

const run = await resp.json();
for (const answer of run.answers) {
  console.log(answer.surfaceKey, answer.answerText);
  console.log(answer.sources);
}

Prefer to let an agent drive it? Point any MCP client at the AgentGEO MCP server and ask it, in plain language, to pull the answers and analyze them. Same data, no glue code. See the Python guide for the full SDK walkthrough.

Start building on your own AI-visibility data

If you want a dashboard for a marketing team, Profound is a solid choice and we will say so plainly. If you are a developer or agency who wants to own the raw answers, embed them in your product, pay by usage, and let your agent do the analysis — that is what AgentGEO is for.