AI Visibility: how to measure and improve it

A growing share of buyers now start with a question to an AI engine instead of a search box, and they act on the answer they get back. If ChatGPT, Perplexity, Gemini or Google’s AI Overviews do not mention you in that answer, you are invisible for that moment of intent, no matter how well your site would have ranked. AI visibility is the measure of whether, and how, you show up there.

This guide defines AI visibility, explains the metrics that actually matter, and shows how to track and improve it.

What is AI visibility?

AI visibility is how often, and how favorably, AI answer engines surface your brand when they respond to questions in your category. It has three parts: presence (do you appear at all?), prominence (how often, and versus whom?), and portrayal (is what they say about you accurate and positive?). A brand can score well on one and badly on another. Being mentioned once in ten answers is a presence problem; being described with an outdated fact is a portrayal problem.

Why it matters now

Search is shifting from a list of links to a synthesized answer, and that answer increasingly satisfies the query on the spot. The practical effect is that a rising share of searches end without a click to any website. In that world, being named in the answer is the visibility, and the old proxy of “where do I rank” stops telling the whole story.

The metrics that matter

Skip the vanity numbers. Three metrics tell you whether you are winning the answer surface.

Two refinements make these more useful. Track them per engine, because your presence on Perplexity can look nothing like your presence on Gemini. And track them over time, because a single snapshot cannot tell you whether a fix worked. The point of measurement is the trend and the gaps, not a one-off score.

How to track it

There are two ways to do this, and they trade off effort against coverage.

By hand

Write out the questions your buyers actually ask, then ask them in each engine and log whether you appear, who else does, and how you are described. This is free and worth doing once to see reality. It does not scale: the prompts multiply, answers vary run to run, and doing it across six engines every week by hand is a job nobody keeps up.

With a scanner

An AI visibility scanner runs those prompts across engines on a schedule, records citations and share of voice, and flags changes. This is what Cituna does: it scans how ChatGPT, Perplexity, Gemini, Claude, Grok and Google AI Overviews answer your buyer questions, shows the exact prompts where you are missing, and generates the schema, FAQ and content fixes to close each gap. If you work in Claude, it also exposes an MCP server so you can query your own scans, gaps and rankings there.

Whichever you choose, pair the answer-side data with the click side. Connecting Google Search Console lets you watch real impressions and clicks move as your fixes land, so AI visibility work ties back to traffic instead of a floating score you cannot bank.

How to improve it

Measurement points you at the gaps; closing them is answer engine and generative engine optimization. In short: make sure the AI crawlers can reach you, structure your content answer-first so the passage is easy to lift, build your brand into a clear and consistent entity, and earn mentions on the sources the engines trust. Then re-scan to prove the lift.

• Start with the fundamentals in the AEO guide and the GEO guide.
• Go per engine with getting cited by ChatGPT, ranking on Perplexity, and Google AI Overviews.
• Add the machine-readable signals, including a considered take on llms.txt.

Measure, fix, prove, repeat. That loop is the whole discipline, and AI visibility is the number it moves.