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How we measure AI visibility

The "how" beneath the "what". Knowledge Engine Optimization (KEO) and Model Mindshare name what we do; this page is how we actually measure and work — sampled, multi-engine, variance-aware, and written down so you can check it.

The terms these methods serve:Knowledge Engine Optimization (KEO)Model Mindshare

What we measure, and why

AI assistants don't answer from a fixed index. They answer from what they retrieved this second and from what they already know — so we measure both: whether you show up in live answers, and whether the model carries a correct version of you. We also measure where citation territory actually lives, and the evidence says it's largely off your own domain: as of December 2025, the strongest measured correlate of AI visibility was YouTube presence (a correlation of roughly 0.737), with domain authority far weaker (Ahrefs). And we separate being named from being merely linked — because as of June 2026, an estimated 61.7% of AI citations were "ghost citations": the page used as a source, the brand never named in the answer (Semrush). We measure against that reality, not against on-page SEO habit.

The seven dimensions

Live AI visibility — whether you show up when the model searches — breaks into seven transparent dimensions, not one black-box number. Each is something you can see and check.

Answer presence
Do you show up in the live answer at all? When someone asks AI in your category and it searches, are you in the response — or absent while a competitor is named.
Answer position
How prominently do you appear? Named up front as a top pick, or buried late. Position shapes whether the buyer even reaches you.
Named, not just cited
Does it say your name, or just use your page? The answer can pull facts from your site yet never name you — a "ghost citation." We separate being named from being merely a source.
Framing
How does the answer talk about you? Favourably, neutrally, or poorly — and in what context. A bad mention isn't a recommendation.
Answer stability
Does it say the same thing each time? AI answers shift between runs. We ask repeatedly and report the range, not a lucky single response.
Citation footprint
Which sources does it pull from — and are you among them? The answer is built from a handful of trusted pages; we track where that territory is and whether you hold any of it.
Intent coverage
Across the buyer's real questions, where do you surface? Discovery, comparison, "best for X," objections — visibility in one prompt isn't visibility across the journey.

The seven dimensions are reported separately, each with its own confidence range. We do not combine them into a single score: a calibrated weighting would be required, and we will only publish one derived from real measurement.

We measure the wobble

Ask an AI the same question twice and the answer moves — wording, sometimes even the names. A single response is noise. So we don't report a single answer: we take the real prompts buyers use in your category, ask them across several engines, repeat, and report the result as a range with a confidence interval — the signal and its wobble together. The same honesty runs across the site: the Model Mindshare meter shows a level on a scale, never a promised number.

What we don't promise

We don't promise a guaranteed percentage, a guaranteed ranking, or a guaranteed mention. It's a sampled signal, not a census of every question ever asked, and it can't be bolted onto your click numbers — they're different kinds of measurement, kept side by side. We report what moves and say what we don't know. The credibility comes from the limits, not despite them.

The playbooks

How the work gets done, method by method:

Sources