What is an AI visibility audit?
What one should contain, what it cannot tell you, and how to judge a vendor selling one.
Taras NetrevFounder, ModelVoxUpdated August 2026An AI visibility audit measures how AI assistants currently describe and recommend a company: whether it appears for the questions its buyers ask, what is said about it when it does, which competitors are named instead, and which sources those descriptions are drawn from.
A good audit produces sentences and sources. A weak one produces a score. The difference matters, because a percentage tells you that something is wrong without telling you what to change.
What a complete audit contains
The questions, chosen properly. Not brand searches — those flatter everyone. The questions that generate shortlists: category questions, problem questions, comparison questions, and the "who should we talk to about X" phrasing buyers actually use. Getting this set wrong invalidates everything downstream.
Verbatim responses across systems. What ChatGPT, Claude, Gemini, Perplexity and Google’s AI Overviews return, recorded as written, with the date and the exact prompt. Paraphrase is not evidence.
The competitive set the model believes in. Who is named alongside you. This is often the most surprising output, because the model’s comparison set frequently differs from the one in your board deck.
A source trace. Which pages and publications appear to be producing each characterisation. This is what converts a finding into a plan — a description you dislike becomes a problem with an address.
A legibility check. Whether your content exists in the served HTML without JavaScript, whether the entity graph resolves, whether crawler policy permits what you intend. This is where the single most common structural cause of invisibility is found.
Variance disclosure. These systems differ between sessions. An audit that reports one run per question, with no account of variance, is reporting noise as signal.
What an audit cannot tell you
It cannot tell you why a model said what it said. The systems are not interpretable from the outside, and any vendor presenting a causal explanation is presenting a hypothesis. A source trace shows correlation and plausible influence — which is useful and is not the same thing.
It cannot tell you your revenue impact. There is no impression data, no click path, and no way to know which buyers consulted an assistant and quietly moved on. Anyone converting an AI visibility score into a pipeline figure is performing arithmetic on an assumption.
It cannot promise that a change will move the answer. It can identify the changes most likely to, and then the answer has to be re-sampled to find out. Treat the whole exercise as an experiment with a measurement plan, because that is what it is.
How to judge a vendor selling one
Five questions separate a serious audit from a dashboard with a proposal attached.
Will you show me the verbatim responses, or only a score? Will you tell me which sources produced them? How many times did you run each question, and what was the variance? What will you recommend if the finding is that our positioning is the problem rather than our markup? And: what would make you tell us not to buy the follow-on engagement?
The last question is the most revealing. A firm whose audit always concludes that a long programme is required has an audit that is a sales instrument. That is not automatically disqualifying — but you should price the finding accordingly.
Sources
Related questions.
The questions that usually come next, answered to the same standard.
Keep reading.
This page answers the general question.
The specific one — what these systems say about your company, and where those words came from — takes a working session and about a week. The finding is yours whether or not we go further.
The first call is complimentary — and the finding is yours to keep.