Visibility you can examine. Progress you can explain.

1. Agree what we are testing

Start with the business, service area, audience and buying decisions. Agree the platforms, pages, question set and accessible accounts in scope. A technical access audit and a market-wide visibility study are not the same service.

Keep branded questions separate from discovery questions. Asking an assistant to describe a named company tests different behaviour from asking it to help choose a provider.

2. Check the foundations separately

Inspect public-page access, indexing controls, canonical URLs, internal links and relevant content. Document hosting checks only where permissions allow them. An accessible robots file does not establish that a firewall permits a crawler.

Google describes AI-search eligibility in terms of existing search foundations. OpenAI documents separate search and training crawler controls. We do not treat training access or a special file as a shortcut to recommendations.

Sources: Google: AI features and your website · OpenAI: crawler documentation

3. Record observations that can be checked

For sampled questions, record the exact wording, platform or product, date, location settings where available, answer and cited sources. Use fresh test contexts, not a conversation already briefed on the client. Repeat observations on the agreed cadence rather than choosing one favourable screenshot.

A business mention, a recommendation and a citation are recorded separately. Results are labelled as visibility in the tracked question set, not total market share or a search-volume estimate. A sample is not every answer every customer sees.

4. Keep visibility and commercial outcomes distinct

Where account access and sufficient data are available, review first-party search reporting and identifiable referral traffic. Then examine completed enquiries, qualified conversations and reported sales separately.

Preserve the distinction between tracked referrals, a customer saying they used an AI assistant and inferred influence. Changes in advertising, seasonality or the offer are logged rather than assigning all improvement to AEO.

5. Explain the work and the next decision

The report lists the changes delivered, supporting evidence, observations and open questions. It ends with the next priority, not a score with no explanation. A useful recommendation may be to improve an existing page rather than publish another article.

Business facts and original client material require approval before publication. Case studies need permission and documented outcomes. Concept designs remain labelled as concepts, not client results.

What this method does not promise

No guaranteed rankings, citations, recommendations or revenue. No claim that buying an ad changes an organic AI answer. No claim that a single prompt establishes overall visibility.

Platforms and responses can change. We report what was observed within the stated scope, retain the relevant evidence and record unavailable data honestly. The method supports decisions; it is not a controlled experiment proving that one edit caused a commercial result.