What should an AI visibility audit of your online store include?

An audit should tell you where to spend effort next and show the evidence behind that choice. A store may need to correct a product record, explain a delivery restriction, improve an important collection page or investigate why competitors appear for a particular purchase. A score without the underlying observations does not tell the owner which of those jobs is worth doing.

Consider a jewellery retailer selling ready-made pieces online and accepting bespoke ring enquiries. Its owner wants more suitable shoppers and consultations. We will use this example to show how to document findings and choose corrections.

For that store, a sensible scope would identify the market, language, relevant collections and services, then separate product shopping from local or bespoke recommendations. It would state which AI services and interfaces were checked. An API test, the ChatGPT website and another assistant's consumer interface should be labelled separately rather than merged into a report called 'ChatGPT visibility'.

Before approving the work, agree what you will receive: the observations, the list of questions, the source URLs, prioritised findings and an explanation of what was not checked. A sample-based audit should name the sampled products and pages. It should not imply that the whole catalogue was inspected because a few representative pages were reviewed.

Each finding needs enough context for someone else to inspect it. Save the exact question, the complete answer and the sources shown with it. Record when and where the test ran, the language, the available model or mode label, whether search was used and relevant account or location settings. Start a fresh conversation for each independent test and describe how personalisation was limited.

Use questions drawn from actual customer needs where possible. Separate questions that name the retailer from those that could introduce it to a new buyer. 'Does this shop resize rings?' tests information about a known shop. 'Where can I order a ring in my size?' tests discovery. Mixing those results can make an already-known brand look easier to discover than the evidence supports.

Repeat the core checks and keep unsuccessful attempts in the log. If an answer was blocked, failed or did not use search, mark that condition. Do not quietly replace a difficult question with an easier one. Where a model label is unavailable, say so; an invented version number makes the report look more precise while making it less reliable.

A useful finding separates the observed problem from the proposed explanation. Suppose a product offer presents a ring setting as though its price includes the centre stone. The immediate business problem is clear: the shopper may arrive with the wrong budget. The cause is not yet clear. The auditor should compare the answer's source, the visible page, the relevant product record and any supplied feed before blaming the AI service.

If the store's title and description imply that the stone is included, with the exclusion buried in a note, revise that information. If the website is clear but a connected catalogue still contains the old description, investigate the record and how it is updated. If the misleading information comes from another retailer or an old third-party article, document that source. These possibilities call for different actions.

Now suppose the shop is missing from a bespoke-ring answer. Absence alone cannot show that the site was blocked, that the copy was weak or that the assistant distrusts the brand. Check what appeared instead, whether the sources addressed the same service and location, and whether the shop has a public page answering that need. The report should preserve uncertainty until there is evidence to narrow it.

For access checks, OpenAI identifies OAI-SearchBot as its search crawler and treats training access separately. The auditor should record what was actually tested, including any hosting restriction. A successful page fetch is evidence of access under those conditions; it is not evidence of a recommendation or proof that a particular assistant retrieved the page.

Prioritise corrections by their effect on the customer and the confidence of the diagnosis. Wrong product identity, price or delivery information deserves attention before a speculative publishing programme. A page that fails to explain a service may need a concise revision. A proposed campaign of outside mentions needs a clearer argument about the audience and sources it would reach.

The table below shows how to turn audit findings into tasks. Notice that completing a repair and checking later AI answers are separate steps. Your team can verify that a setting is labelled correctly on the website. It cannot declare that every AI answer has changed just because the content was updated.

Report the observations separately before calculating a percentage. A brand mention is the business being named. A citation is a link to a source. A shopping offer identifies a product and seller in the shopping interface. None of these, on its own, is a recorded visit or sale. Decide which observation each metric counts and show its denominator.

For repeated tests, distinguish the number of questions from the number of completed answers. State how many questions produced at least one citation and, separately, how many answer runs contained a citation. Those measures can differ. Keep failures visible beside the completed sample. Show the actual counts before percentages so a small change cannot hide behind a large-looking relative increase.

An audit can also record factual accuracy: was the named item correct, was the seller correctly identified, and did any quoted price, availability or service claim match the source checked at that time? For changing stock and prices, preserve the timestamp. A screenshot compared with today's catalogue may not establish what was true when the answer was produced.

Add business outcomes from analytics and enquiries where they are available. Label their attribution limits. Google documents that traffic from its AI search features is included in Search Console's Web performance reporting; an overall Google traffic change therefore cannot, by itself, be labelled an AI-only result. Likewise, a homepage visit attributed to ChatGPT does not tell you the visitor's exact prompt or prove which article caused the recommendation.

Ask for the dated observations behind any before-and-after claim. Changes in the test questions, interface or service offering may explain a difference. A report can show that visibility moved during a period of work without proving that one content edit caused the movement.

Ask to see a sample report before discussing the score the audit might produce. You should understand the scope, where the customer questions come from, the access checks and the tasks your team will receive afterwards. Agree who can make changes and which evidence you will retain if you choose another provider.

If product-feed work is included, ask whether the provider will inspect an existing connection or build a new one. OpenAI's direct product-feed onboarding is limited to approved partners as checked on 20 September 2026. The proposal should explain the relevant eligibility and ownership; 'we will submit your products' is incomplete when approval or another platform's arrangements are involved.

Our free AI visibility audit covers ten prompts in one language, current answers, crawler access and concrete suggested fixes. That is an initial investigation. It is not a claim to inspect every item in a store or provide a complete commercial attribution study. A wider engagement needs an agreed scope for categories, markets, repeated checks and implementation.

Use the sample to judge the reasoning. Does the proposed correction address an evidenced problem? Is there a responsible person and a way to check the repair? Can you distinguish what was found from what the provider suspects? Those answers make an audit useful even when the first conclusion is to fix a small part of the store before commissioning more content.