AI Discoverability Audit: Know What AI Search Is Saying About You Before You Commit to a Retainer

What The Audit Actually Measures

This AI Discoverability Audit checks the answer engines buyers use: ChatGPT, Perplexity, Gemini, Google AI Overviews, and Microsoft Copilot. It runs across the real questions a buyer asks, from an early category search through the final shortlist pick.

What comes back is a plain read: how often you get named, and how that stacks up against whichever rivals are winning the mention instead. A deeper pass follows underneath, covering schema and content freshness, but that stays an input to the strategic read, not the deliverable itself.

The finding lands in one of three places. Your position holds up and the gap sits elsewhere, or a named rival is winning the mention and needs a direct answer. Or the gap is real but isn’t worth fixing yet.

When This Reaches The Board

A named rival keeps showing up in AI answers for terms you believe you should own. You want a straight answer on why, and whether it can even be fixed before you spend on a workaround.

An agency is pitching a GEO or AEO retainer priced in the thousands each month. You want a plain read first, on whether the problem they describe is even real.

AI-led shortlists keep favoring a rival while organic pipeline has quietly stalled. You need to know whether the two are linked before deciding what to do about either one.

Your SEO vendor says the site is AI-ready after a schema check. Nobody has checked whether your brand gets named, and that gap is the one that matters most right now.

How is this different from an SEO audit?

An SEO audit checks how a site ranks in search results. That’s a separate question from whether your brand gets named when a buyer asks an AI system who to trust. A site can rank well and still be invisible in that answer, and that gap is what this audit checks.

Does this matter for a smaller or niche company?

It often matters more than it does for a large brand, since AI answer engines tend to default to whichever names are easiest to find. A smaller company with less content weight can get left out, and the audit shows exactly where that happens.

How The Full Diagnostic Runs

The engagement opens with a working session on your category, your named rivals, and the buyer questions worth asking. From there, Dromley builds a prompt set from how your real buyers search, not a generic template.

That set runs across every major answer engine, showing how often you’re named and how your share stacks up against the rivals you picked. A senior partner reviews the pattern by hand and traces which sources get named in your place.

The engagement closes with a single strategic read: where the gap sits, why it exists, and the one priority that matters most right now. There is no fixed list of what you get, and no separate team turning the finding into something else.

How do you measure something this new?

By running real buyer prompts against the answer engines your buyers use, then counting who gets named and how often. The method stays plain enough for you to check yourself, and where the research base still runs thin, the read says so instead of faking certainty.

What happens after the audit?

You get one strategic read and a single named priority, with nothing more assumed on top of it. Some findings point to a content gap, some to a site issue, and some just aren’t worth fixing yet, and that call stays yours alone.

What The Read Has To Survive

Independent, first: Dromley doesn’t sell schema fixes or the retainer this read might point toward, so the finding is never shaped by more billable hours.

Named sources, too: the questions asked, the engines checked, and how the count was reached get stated plainly, so any skeptical reader can check the method.

A weighed call: this field is still new. The read states plainly what today’s evidence backs, and where it runs thin, not faking answers it hasn’t earned.