Coverage

The AI platforms we track

Six assistants, each collected its own way and reported on its own terms. We tell you how every answer was obtained, because the collection method changes what the number means.

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Engines · named · last 30 days

  • ChatGPT

    47%n=30

    Search product surface

  • Claude

    30%n=30

    API with web search

  • Gemini

    57%n=30

    Search product surface

  • Perplexity

    40%n=30

    API with web search

  • Google AI Overviews

    20%n=30

    Live SERP

  • Google AI Mode

    3/4n=4

    Live SERP

Illustrative figures from a demo workspace, not a customer's data.

Every engine we measure

Pick one to see how it behaves and what makes it different to measure.

Why the collection method is on every number

A scraped product surface and a model API answer are not the same measurement.

One reflects what a person sees in the product, including whatever personalisation and interface changes ship that week. The other reflects what the model returns when asked cleanly. Both are worth knowing and they are not interchangeable, so we label every figure with how it was obtained rather than presenting a single number whose provenance you would have to take on trust.

The full rules are in the methodology, and AI Overview tracking shows how it plays out on one surface.

Questions about coverage

Why not combine every engine into a single visibility score?
Because the engines retrieve differently and frequently disagree, so an average describes nothing that exists. A brand named by Perplexity on nine answers in ten and by ChatGPT on none has a specific, fixable problem. The average of those two numbers hides it.
Which engine matters most for my brand?
Usually the one where your buyers already are, which is rarely the one with the biggest headline user count. Perplexity cites most heavily so it reads your retrieval most clearly, Google AI Overviews reach the largest audience, and Claude skews senior. We track all of them so you do not have to guess in advance.
Do you track Grok, Llama or DeepSeek?
Not as visibility measurements. Those models answer largely from training data without live retrieval, so what they say reflects a snapshot of the past rather than anything you can influence this quarter. Reporting them next to retrieval-based engines would imply a comparability that does not exist.

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