AI Visibility Audit
Your competitors show up when someone asks ChatGPT who to hire. You do not, and you cannot tell why.
What You Get
Everything in the Technical Audit, plus a measured baseline of your AI visibility, the starting point for generative engine optimization (GEO): 25 to 30 real buyer questions run across ChatGPT, Perplexity, Gemini, and Google AI Overviews, every answer captured, with who appears, who is cited, and which sources the answers came from. Competitor citation analysis, entity and name-collision review, content structure review for machine readability. Report, ticketed roadmap, walkthrough, and a 90-day re-measure of both the technical baseline and the same locked query set.
What you receive
The report, the query data, and a ticket set a developer can execute. Here is a redacted report from a recent engagement and the ticket index that came with it, client details withheld.
Price
From $6,000. Additional office locations and multilingual sites affect the price. Quoted up front.
Questions
What is an AI visibility audit?
It measures whether AI assistants mention, recommend, or cite a business when people ask them buyer questions, and why. It combines a query baseline across the major platforms with a technical review of the pages and signals those platforms read.
How is AI visibility measured?
By running a fixed set of real buyer questions across the major AI platforms in fresh sessions, recording every answer, and running the identical set again later.
Is this the same as search engine optimization (SEO)?
It builds on the same technical foundation and adds the layer AI systems read: entity clarity, content that answers questions directly, and the third-party sources AI engines cite. The same site can rank on Google and be invisible to ChatGPT.
What exactly do you measure, and how?
We run 28 buyer questions, built from your intake and our own research and approved by you before the first run, across ChatGPT with search on, Perplexity, Gemini, and Google AI Overviews. Every run is a fresh, logged-out session from a fixed US location, with the date, environment, and model version recorded alongside the full response. At 90 days we run the identical set the same way, plus a variance sub-study where seven queries are run three times each, so movement is read against normal run-to-run noise. Platform changes between the two measurements are recorded as confounds, not ignored.