AI Search Visibility Audit

An AI search visibility audit maps the exact gaps in your brand's presence across AI engines - not just a score, but a matrix of which prompts and engines miss you, which competitors fill those gaps, and a prioritised list of fixes ranked by impact. CitedSpy queries ChatGPT, Perplexity, Gemini, Claude and Grok live and returns a structured audit report in minutes.

A score tells you where you stand; an audit tells you where you are broken and what to fix first. Traditional AI visibility scores compress all the signal into one number, which is useful for tracking but not for acting. An AI search visibility audit surfaces the underlying structure: a matrix of every prompt tested against every engine reached, showing exactly where your brand appears and where it is absent - the specific combinations a score cannot show.

That gap matrix is the starting point for diagnosis. It is common for a brand to appear reliably on Perplexity, which leans on live web search, while being absent on Claude or Gemini, which weight training data more heavily. A brand might win the 'best tool for X' prompt on three engines while going unmentioned on 'how to do Y' prompts that attract the same buyers. A score averages those patterns together; an audit exposes each gap individually so you know which one to close first.

The second layer is competitor overlap: who fills the space your brand leaves? When AI engines do not name you on a prompt, they name someone else - and that name is actionable information. Knowing that a specific rival consistently appears where you are absent is far more useful than knowing your overall mention rate is low. It tells you exactly whose authority in the engines' view you need to match, and what content they publish that you do not. This is what makes an audit different from a checker: it gives you a specific competitive brief, not a general score.

The third layer is a prioritised fix plan derived directly from the gap data. Rather than generic GEO advice, each fix is grounded in the specific audit finding it addresses - the worst engine, the most-blocking competitor, the prompts where you have partial coverage and are therefore closest to full closure. Generative Engine Optimization works when it is targeted; an audit is the targeting mechanism.

Start with the health label above the matrix. 'Good visibility' means gaps are minor and spread - no single engine or prompt is badly broken. 'Patchy visibility' means there are meaningful clusters of absence worth fixing. 'Critical gaps' means your brand is largely absent and the audit's fix plan is where to invest first. The label is derived from your gap rate: the share of possible appearances (prompts times engines reached) where you were absent.

Then scan the matrix row by row. A prompt where you are absent on all engines is a category-level coverage failure - engines simply do not associate your brand with that query at all. A prompt where you appear on two or three engines but miss one or two is a partial gap, the easiest to close since you already have some authority there. The gap count column on the right makes the worst rows visible immediately.

The competitor overlap section tells you who occupies your gaps. If the same rival appears across most of your gap cells, that is your benchmark: their authority in the content AI engines cite is what you are missing. The fix plan translates this directly - target the specific rival with comparison content, earn mentions in the same authoritative sources they appear in, and fill the gap at the engine level where they beat you most.

The fix plan is prioritised by expected impact. Priority one is usually the highest-volume gap: earning third-party mentions if you are broadly absent, or targeting the engine with the most missing cells if your gap is concentrated. Working down the list in order is more efficient than spreading effort across all recommendations at once. Once a fix closes a gap, re-run the audit to confirm and find the next priority.