An AI visibility Audit is a structured review of how answer engines find, understand, mention, and cite a business. It looks at the answers buyers receive, the sources behind those answers, and the website signals that help the engines decide what to trust.
The useful output is evidence and an ordered plan. You should leave knowing where the business appears, what the engines get wrong, which competitors or third-party sources shape the answer, and what to change first.
What the Audit examines
A credible Audit connects four kinds of evidence.
The answers
The Audit asks a defined set of questions across selected AI systems. These should reflect real buyer decisions: category searches, comparisons, evaluation questions, branded questions, and problems the buyer is trying to solve.
The responses show whether the business is present, how it is described, and whether the description is accurate. They also show which competitors appear and which sources earn citations.
The source pool
Answer engines build responses from more than the company’s website. They can draw from directories, review platforms, editorial coverage, partner pages, social profiles, old articles, and competitor content.
An Audit should identify the sources that shape the category. That turns a vague recommendation such as “build authority” into a specific publishing, profile, or outreach decision.
The website
The technical review checks whether the important pages are reachable and understandable. That includes crawler access, sitemaps, canonical URLs, structured data, visible identity, content freshness, page performance, and the consistency of facts across the site.
Technical access is the entry requirement. It does not guarantee a citation. The site also needs clear answers and credible proof.
The plan
Evidence becomes useful when someone decides what matters first. A strong Audit connects each finding to a page, source, or technical change; explains why it matters; and gives the team a definition of done.
How an Audit differs from a Scan
A Scan is a fast snapshot. It is useful for finding the first visible gaps and deciding whether deeper investigation is warranted.
An Audit uses a broader prompt set, more answer surfaces, deeper source review, and human judgment. It should reconcile conflicting signals and prioritize the work. That is the part a dashboard cannot do on its own.
Agentsy Report follows that split. The free Scan asks seven questions across ChatGPT, Gemini, and Perplexity, then checks public technical signals. The human-reviewed Audit tests 24 prompts across seven measured AI surfaces and delivers a prioritized, page-by-page plan.
What the result can prove
An Audit can show what the selected engines returned during the measurement window. It can document mentions, citations, accuracy problems, source patterns, technical barriers, and gaps in the site’s content.
It cannot promise a permanent answer or guaranteed citation. AI systems change. Their indexes change. The same prompt can return a different response later. That is why the prompt set, date, provider, and evidence should remain visible in the report.
When to commission one
An Audit makes sense when AI answers could influence a meaningful buying decision and the business needs more than a list of generic recommendations.
Common triggers include:
- The business is absent from category or comparison answers.
- AI describes the company inaccurately.
- Competitors appear consistently and the reason is unclear.
- The team has already done basic SEO work but does not know what to change for AI search.
- Leadership needs a shared, prioritized plan before assigning work.
Start with the free Scan if you need the first snapshot. Choose the complete Audit when you need the evidence reviewed and turned into work your team can ship.
