Position · Service 02 — AI Search Visibility Audit

Read the sentence AI systems produce about you.

We run the questions your buyers actually ask across ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews, record what comes back verbatim, and trace each description to the sources that produced it. Most companies have never seen this.

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You cannot fix a description you have never read.

What the audit is

Not a score. A document with sentences in it.

There is a growing market in AI visibility dashboards that report a share-of-voice percentage and a trend line. Those are useful for tracking and useless for deciding, because a number tells you that something is wrong without telling you what to change.

This audit produces the sentences. What each system says about your company, for the specific questions that generate shortlists in your market, alongside the companies it names instead of you — and then the source trace: which pages, publications and third-party mentions produced each characterisation.

The source trace is the part that makes the finding actionable. A model’s description is assembled, not invented. Once you know that three specific pages are producing the sentence you dislike, you have a problem with an address.

Built for Companies absent from answers where competitors appear Categories with three interchangeable leaders Teams instrumenting AI visibility for the first time Boards asking a question marketing cannot yet answer

Deliverables

What you walk away with.

01 The Default Sentence reportWhat ChatGPT, Claude, Gemini, Perplexity and AI Overviews say about you, verbatim, across the questions that produce shortlists.

02 Source traceThe pages and publications shaping each description, ranked by apparent influence — the map of what to change first.

03 Competitive readThe same collection run on the two companies you are most often confused with, side by side with yours.

04 Legibility findingsWhether your content survives a crawler that does not execute JavaScript, whether your entity graph resolves, and what your crawler policy actually permits — which is often not what you intended.

05 Corroboration distanceFor each claim that matters, the count of independent sources currently repeating it. The number that predicts whether an answer will move.

06 Prioritised roadmapThe changes ordered by expected effect on the answer, separating what can move in weeks from what compounds over quarters.

Why it matters now

The failure mode is silent, and no dashboard you own reports it.

A company skipped in an AI answer sees nothing. No impression, no bounce, no lost-deal reason code. The buyer never arrives, so there is no signal that they existed. Analytics is structurally incapable of reporting this.

Which means the first honest measurement most companies get is an audit. It is usually uncomfortable and it is usually the most useful document the marketing team has read that year, because for once the criticism is not an opinion.

Questions

On ai search visibility audit.

The objections worth raising before a first call, answered as we would answer them on one.

Is this just SEO with new vocabulary?

No, though there is real overlap in the technical foundation. Traditional SEO optimises for position in a list of links. This measures how a system describes and recommends you in a synthesised answer, which is driven by specificity and corroboration rather than by ranking factors. The technical layer is shared; the strategy is not.

Can you guarantee a model will recommend us?

No, and treat any firm that guarantees it with suspicion. These systems are non-deterministic, they change without notice, and nobody sells access to the ranking. What can be done is to change the inputs — clarity, specificity, corroboration, legibility — and measure whether the answers move. We report the ones that did not as prominently as the ones that did.

How fast do answers change?

Unevenly. Retrieval-based systems that fetch live pages can reflect a change within days. Answers drawing on model priors move on a much slower cadence, and corroboration takes months to accumulate by design. The roadmap sequences the fast, cheap fixes ahead of the slow, durable ones so that something moves early.

Do you need access to our systems?

No. This runs entirely on publicly available signals, which is also what the models are working from. No code access, no analytics credentials, no onboarding. If we later rebuild pages, that requires access — but the audit does not.

How often should this be re-run?

Quarterly is enough for most companies, and monthly only if a programme is actively running. Re-running weekly produces noise: the systems vary between sessions, and mistaking that variance for progress is the most common measurement error in this category.