The Scrunch AI alternative for developers

AgentGEO is a Scrunch AI alternative built for developers. Scrunch AI is an enterprise AI-visibility platform: audits and dashboards showing how AI engines present your brand, typically bought through a sales-led motion. AgentGEO takes the infrastructure shape instead - one API and one MCP server that return the raw answers ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini actually give, with citations, sources, and provider metadata. No demo call: the free audit shows you your own data first.

Scrunch AI is a genuinely strong product for its audience. Enterprises that want a managed platform, ready-made audits, and a vendor relationship get exactly that, and there is real value in it. AgentGEO serves the builder on the other side of that motion - the engineer, indie team, or agency that wants raw answer data as a primitive, priced by usage, available today.

The best Scrunch AI alternative for builders

The comparison here is less about features than about motion. An enterprise platform is bought: demos, scoping, contracts, onboarding. Infrastructure is adopted: read the docs, make a call, ship. Both are legitimate - but if you are the one writing the code, the second motion is usually the one you want. Four properties follow from it:

Data ownership by default

AgentGEO returns raw JSON - an answers[] array with each engine's answerText, its sources[] (title, URL, position), and the surfaceKey and providerFields behind every response. It lands in your systems from the first call: store it, join it, build audits from it, delete it. A platform's audits live in the platform; the answer-access layer's output lives wherever you put it.

Embed it, white-label it

Because the product is an API plus MCP server rather than a portal, AI-visibility data can ship inside your product: a feature in your SaaS, a module in your agency's client reporting, a table in the warehouse your executives already query. Nothing about the design assumes your users log into a vendor's UI - the design goal is that they never see one.

Usage-based, with a spend cap

Pricing is run-based, metered by usage, with a spend cap you control - not an annual contract sized in a sales process. A side project pays like a side project; a production embed pays like production. Evaluating it costs nothing: the audit is run by hand for free, and ag_test_ keys return demo records at zero credits.

MCP-native

AgentGEO ships as an MCP server (npx -y agentgeo-mcp), so the analysis layer can be your own agent. Claude Code, Cursor, Codex, or any MCP client pulls live answers mid-task and reasons over them with your context - your docs, your positioning, your codebase. An enterprise dashboard reports to you; an MCP tool works for you, inside the tools you already use.

Scrunch AI vs AgentGEO at a glance

Two shapes for two buyers. Neither is wrong - the rows below tell you which one you are.

Self-serve, from the first call up

With a sales-led platform, the first look at your own data sits behind a demo call, a scoping conversation, and a contract. That is normal for enterprise software, and for enterprise buyers it works. But if you are an engineer evaluating on a Tuesday night, the calendar invite is the friction.

There is no procurement cycle here. Start with the free audit - no card, no account - and a real person sends back what ChatGPT, Perplexity and Gemini say about you. From there, one key runs the same fetches yourself, and it scales through subscription tiers with metered overage and a spend cap, so nothing is re-negotiated between the first script and the production embed. The docs are public and pricing is published, so you can price it before you commit to anything.

Everything an audit is built from

An AI-visibility audit is an analysis over answers and citations. AgentGEO returns the full set of primitives, so your audit - or your agent's - starts from evidence:

• Six engines: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini - one interface, per-request selection via surfaces.
• Verbatim answer text - how each engine actually presents your brand, unsummarized.
• Every cited source with title, URL, and position, so you see exactly who the engines trust.
• Provider metadata (surfaceKey, providerFields) for attribution and change tracking.
• Managed-scraper engine, maintained for you - no headless browsers or proxies underneath your compliance review.

The whole API in one call

One authenticated POST is the entire surface area. Extend surfaces to fan out across engines.

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

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

Full walkthroughs - retries, storage, and multi-engine fan-out - live in the Python guide and the Node guide.

Start before the demo call would have been scheduled

If your organization wants a managed enterprise platform with audits delivered to it, Scrunch AI is a strong pick and we will say so plainly. If you would rather hold the raw answers yourself - self-serve, usage-priced, embeddable - you can have a working key before a discovery call would have cleared anyone's calendar.