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How to run an AI visibility audit (a repeatable method)

An AI visibility audit has four steps: write down the questions your buyers actually ask; put them to several engines repeatedly and record who is named, recommended and cited; check whether your site can be read and cited by AI crawlers; then rank fixes by which failure you actually have. The traps are sampling once, measuring one engine, and asking about yourself by name.

Updated 22 July 2026

You can buy this as a product — ours is the index — but the method is not a secret, and a rough audit run honestly beats a polished one run wrong. Here is the repeatable version, with the traps marked.

Step 1: write the questions buyers ask

Not questions about you — questions a buyer with money asks before knowing you exist: "where can I buy X online in the UK", "best shop for Y", "who delivers Z by Friday". Ten to twenty of them, phrased the way real customers phrase things. Mine them from your search-console queries, your customer emails, and the forums in your category.

Trap: asking "what do you know about [your brand]?" That measures recognition, not visibility — an assistant can describe you fluently when asked and still never volunteer you to a buyer who didn't.

Step 2: sample, don't ask once

Put each question to each engine several times, ideally across days. Answers vary run to run — the same question can name you Monday and skip you Tuesday — so a single ask is an anecdote. Record, for every answer: which brands were named, which were actively recommended, and which sources were cited.

Trap: one engine. They disagree seven-fold about the same brand, so a ChatGPT-only audit is a ChatGPT audit. Cover at least ChatGPT, Gemini and one citation-heavy engine like Perplexity.

Step 3: count honestly

Three numbers per brand: mention rate (share of answers naming it), recommendation rate (share actively steering to it — far rarer, and the one that moves sales), and citation rate (share citing its site). Then a share of voice — your mentions over everyone's — remembering the denominator must include every brand the answers named, not just the rivals you already watch. Write down who the assistants cited: that list of sources is your actual battleground.

Trap: treating small differences as findings. At modest sample sizes, a few percentage points is noise. Trends across repeated audits mean something; wobble does not.

Step 4: check the readable half

While the sampling runs, audit whether AI can read you at all: crawler policy (including your CDN's bot defaults), JavaScript-dependence, structured data, and whether your key pages answer a buyer's question in a liftable first screenful. This is the fast half to fix, and it gates everything else.

Turning it into decisions

The audit's output is a diagnosis: unreadable site → fix this month; absent from the roundups and threads the engines cite → start the editorial and community work now, it is slow; named but never recommended → study what the cited sources say about the brands that do get the nod. Re-run quarterly with the same questions, same engines, same counting — an audit's value compounds only if successive readings are comparable.

Or start with the readable half in two minutes: run a free scan.

Common questions

How many questions and samples does a credible audit need?
Enough that rates stabilise: as a floor, 10–20 questions, each asked 2–3 times per engine, across 3+ engines — a few hundred answers. Fewer than that and treat the result as directional. Published measurements should carry confidence intervals; ours do, because sampled answers are genuinely noisy.
How often should I re-audit AI visibility?
Quarterly for most brands — retrieval-led answers move in weeks, trained memory in model-release cycles, so monthly re-audits mostly measure noise while annual ones miss real shifts. Re-run with the same questions and method each time, or the readings aren't comparable and the trend is fiction.
Can I just ask ChatGPT to audit my AI visibility?
It will produce something confident, but it cannot sample its own answers systematically, cannot see other engines, and tends to flatter the brand doing the asking. Use assistants as the thing being measured, not the measurer — the counting has to happen outside the engine.

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