What an AI entity audit actually contains

If "AI search visibility" sounds like a category that doesn't quite have a method yet, it's because it doesn't. Everyone's still working it out.

But the diagnostic underneath any sensible programme is the same one journalists do when they're checking who knows what: ask the source, write down what they say, compare it against what's actually true. The AI engines are just sources we're asking on behalf of every prospective client who's started asking the same questions.

Here's what an entity audit contains and why each piece matters.

What an entity audit is

It's a structured probe of what the major AI engines currently know - and currently believe - about your agency, your founder, and your category. Run as a snapshot, with the exact prompts logged, the exact answers logged, and the diffs across engines recorded.

It serves three purposes:

1. Baseline. Without a baseline you can't see whether the programme is working. The engines refresh on their own schedules; without a fixed prompt set, you're guessing.
2. Diagnosis. Every wrong answer points at a fixable thing on your site, schema or earned-media footprint.
3. Prioritisation. Most agencies have three or four fixes that would move the needle. The audit tells you which ones.

A good audit is boring to look at. It's a spreadsheet. Each row is a prompt, each column is an engine, each cell is a verbatim answer plus a note on what's accurate, what's wrong, and what's missing.

The twelve prompts

There's no fixed canon. The set we run divides into three groups:

Entity prompts - what does the engine know about you specifically?

• "What is [your agency]?"
• "Who founded [your agency] and what's their background?"
• "What services does [your agency] offer?"

Discovery prompts - what would your buyers ask?

• "Recommend a [your category] agency for [specific client type]."
• "Which [your category] agencies specialise in [your differentiator]?"
• "What's the best [your category] agency in [your geography]?"

Authority prompts - does the engine place you in the right category at all?

• "Who are the leading [your category] agencies?"
• "What's the difference between [your category] and [adjacent category]?"
• A couple of long-tail variants relevant to your specialism.

That's twelve. Run them against ChatGPT, Claude, Perplexity and Gemini. Save the date, the model version, and the verbatim answer.

What the diffs tell you

The pattern of mistakes points at the fix.

Entity prompts wrong or thin → on-site canonical descriptions are inconsistent. The engines synthesise from what's published. If your homepage says one thing, your About page says another, and your LinkedIn says a third, the engines pick the strongest signal - which is usually the one with the most third-party reinforcement, and which may not be the one you want them to pick. Fix: write a single canonical description. Use the same words on the site, in the schema, in your bios, and ask future bylines to credit you with the same wording.

Discovery prompts ignore you → your specialism isn't documented enough. The engines can only recommend you for what they have evidence of. If you say you specialise in something on the homepage but nowhere else, the engines won't trust the claim. Fix: publish answer-shaped pages that name the specialism in the first paragraph, plus earned media that corroborates.

Authority prompts misplace you → the citations are pointing somewhere else. The engines weight third-party mentions heavily. If most of your earned media is in a different category than where you want to be, the engines will follow the citations, not the homepage. Fix: pitch into the category you want to own, not the category you happen to have history in.

The Organisation and Person schema

The hygiene fixes - the cheap ones - are usually on-page.

The Organisation schema (@type: Organization in JSON-LD, since that's the literal schema.org type) in your site's root layout should contain: your name, alternate names, the canonical description, your founder credit, your address, your social profiles (sameAs), and the services you offer (makesOffer). If the engines synthesise an answer about your services, the language they use is usually pulled from this object.

The Person schema on the About page should contain the founder's name, role, history (alumniOf, worksFor), and the same sameAs social URLs. This is what makes the founder credit you put on every byline actually stick - the engines triangulate the credit against the schema.

These objects are not magic. They're just structured copies of facts you already publish. The reason they help is that they remove ambiguity from the synthesis step.

What the audit isn't

It isn't an SEO audit. SEO optimises for being one of ten blue links; AI visibility optimises for being the synthesised answer. The mechanics overlap but the goalposts are different.

It isn't a one-time exercise either. The engines change. Your category changes. Your earned-media footprint changes. An audit that's older than six months is mostly storytelling.

What to do with the result

The output of an audit should be a short prioritisation memo, not a deck. Roughly:

1. Three on-site fixes this week. Canonical descriptions, schema, the first answer-page rewrite. These move ChatGPT and Claude fast.
2. Three off-site pushes this quarter. Pitches into the category you want to be cited as. These move Perplexity first, then the rest.
3. A monthly re-run. Same twelve prompts. Same four engines. A spreadsheet that gets longer every month.

The discipline is the deliverable. Most agencies do an audit once, get scared, and stop. The ones that move are the ones that re-run it monthly until the answers change.

That's how this category gets won. Slowly, on purpose, in public.