An AI Visibility Audit That
Ends the Guessing

It's a fixed-scope diagnostic that measures how AI systems currently see and describe your brand. It covers four areas: whether AI crawlers can access and render your pages, whether models can resolve your brand as an entity, whether your content is structurally extractable, and what share of your category's answers currently name you.

The output is a written findings report, a prioritized 90-day roadmap, the raw benchmark dataset, a competitor gap map, a developer-ready technical spec, and a walkthrough call.

Two to three weeks from kickoff to the walkthrough call. The main variable is how quickly you can provide access — Search Console, analytics, and a crawl of the live site.

Very large sites or complex multi-property setups can run slightly longer, and we'll say so upfront rather than discovering it mid-engagement.

No. It's deliberately structured as a standalone product with no obligation attached. A meaningful share of clients run the audit, take the roadmap in-house, and execute it themselves.

If you do proceed to a retainer, the audit fee is typically credited toward the first month.

Standalone audits typically run in the low five figures, with scope driven by site size, catalog complexity, and how many properties or markets are in play.

We quote a fixed fee before starting. There are no variable costs or scope surprises once it's agreed.

Google Search Console, analytics, and permission to crawl the live site. For ecommerce, product feed access helps considerably. For multi-property setups, a list of every domain and subdomain in scope.

We don't need CMS or code access for the audit itself — that's only relevant if you engage us for implementation afterwards.

Partially. We can review architecture, rendering strategy, schema plan, and content structure against staging. What we can't do is benchmark citation share, because there's nothing indexed to measure.

For pre-launch sites we usually recommend a scoped architecture review instead, then a full audit once you've been live long enough to have a baseline worth measuring.

We combine your existing query data, competitor positioning, and the questions your sales team actually hears from buyers. Then we test how those get phrased conversationally, which is often quite different from how they're typed into a search box.

You review and approve the prompt set before we run it, so the benchmark measures questions you actually care about rather than ones we found convenient.

Then we'll tell you, and the audit will be shorter and cheaper in its recommendations. It happens occasionally, usually with brands that have strong technical foundations and genuine category authority already.

We'd rather deliver that honestly than manufacture urgency. A findings report padded with non-issues to justify a fee is worse than useless.

Substantially. A classic SEO audit examines crawlability, performance, and ranking factors for search engines. This tests AI crawler access specifically per user agent, entity resolution, passage extractability, and citation share against a benchmarked prompt set.

There's overlap in the technical layer, and we flag classic SEO issues we find. But the framing, prioritization, and roughly half the tests are different.

Yes, and it's a common outcome. The technical remediation spec is written to be handed straight to a developer — actual directives, actual markup, actual rendering changes, with enough context to understand why each matters.

Where teams typically get stuck is the entity and citation work, which is less about code and more about sustained external effort. We'll be clear about which findings need specialist help and which don't.