MentionBeat is a GEO platform that measures whether AI assistants name your brand, then turns what it finds into fixes you can ship and prove.
Where each surface sits in the cycle
Measure — buyer prompts run across every engine on a schedule, sampled enough times to give a rate with an interval.
Improve — findings become a prioritised queue that closes only when a re-check confirms the page changed.
Prove — a baseline before and a treatment after, reported with a confidence interval.
A number with no next action is a report, and a report changes nothing.
Most tools in this category stop after the first move. A number with no next action is a report, and a report does not change what ChatGPT says about you next month.
A suite of buyer prompts runs on a schedule across every engine, each asked several times, so the result is a sample with an interval rather than one screenshot.
Audits, crawler checks and lost prompts become a prioritised queue. Each item closes only when a re-check confirms the page actually changed.
A baseline run, your change, a treatment run. The result is a difference with a confidence interval — including when the honest answer is "no measurable change".
Each has its own page, because each answers a different question your team will actually ask this quarter.
| Surface | The question it answers | What you leave with |
|---|---|---|
| Visibility tracking | Where do we stand, and is the change real? | Visibility Index, share of voice, mention and recommendation rate — each with an interval |
| Answers & competitors | What did the engine actually say, and who beat us? | Verbatim answers per prompt and engine, a rival leaderboard, the domains being cited |
| Fix queue & site health | What do we fix first, and did it land? | A crawl-wide audit, per-crawler access verdicts, and a queue that closes on verification |
| Content Studio | What exactly do we publish? | Page families built from your approved facts, scored 0–100 before they ship |
| Accuracy & corrections | What are engines getting wrong about us? | Wrong facts with the source they came from, plus drafted correction requests |
| Experiments & proof | Did any of this work? | Measured lift between two runs, attributed to the change that caused it |
| Integrations & API | How does this reach the rest of our stack? | REST API and keys, Slack and email digests, CSV and PDF export, CMS publishing, SSO |
| Credits & plans | What does a run cost before we start it? | Priced runs, weighted by engine, with a ceiling you set |
Nothing, at the start. You can run a first measurement with a domain and nothing else — MentionBeat reads your site, proposes a product brief and a prompt suite, and you edit both before anything runs.
Every number carries how it was produced. A result is labelled live, modeled or estimated; a rate carries its confidence interval; a run that could not reach an engine says so rather than quietly averaging over the gap.
The full method — including its limits and what we deliberately do not do — is published, not summarised.
See where you actually stand across ChatGPT, Claude, Gemini, Perplexity and AI Overviews — then decide how much of the loop you need.