AEO Copilot

About AEO Copilot

AEO Copilot tracks whether ChatGPT, Claude, Perplexity and Google AI Overview name your brand when someone asks the category question that precedes a purchase, and it is built around the assumption that the person doing the tracking is running an AI agent rather than a marketing department. You write the questions your customers actually ask — or let onboarding draft the first nine — and each one runs across all four engines on a schedule you set, so you are not copy-pasting the same prompt into four chat windows every week. The product reads each response rather than just string-matching your name: it records whether the brand got named at all, where it ranked in the answer, which sources the engine cited instead of you, and how the tone came across. Recommendations come back ranked by impact and tied to the specific prompt that triggered them, which is the part most visibility dashboards skip. The distinguishing feature is the delivery surface. Everything is exposed over MCP, so Claude Code, Codex, Cursor, OpenCode or any MCP-speaking assistant can pull the current standing and the fix list into a working session, and there is a REST API on the same tier for non-MCP clients. That matters if you want the weekly check to happen inside an agent loop instead of inside a browser tab you forget to open. Positioning is explicitly against enterprise AEO suites: the pitch is freelancers, small agencies and AI-native startups who run AEO themselves and want proof over time without hiring anyone. The obvious limitation is prompt volume — the plans are sized in prompts per month, and four engines burn through them fast.

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