You cannot fix what you have not measured.
Most businesses assume AI assistants either know about them or don't. The reality is more complicated. A model might know your name but get your category wrong. It might recommend you for the wrong services. It might list an old address. It might name a competitor in response to a query where you should be the obvious answer.
Each of these outcomes has a specific cause — a missing schema field, an inconsistent directory listing, a service page that never plainly states what it does, a Google Business Profile with thin or outdated information. The audit finds the causes. Without it, GEO work is guesswork: you might invest in content that isn't the limiting factor, while a two-minute schema fix would have moved the needle immediately.
This is why an audit is the first step of every GEO engagement at TRIVOX. The fastest path from invisible to cited is a clear gap report, prioritized by impact. See the broader context in what is GEO and the full set of signals in GEO ranking factors.
An AI visibility audit covers five areas.
The audit is a structured review — not a single prompt to ChatGPT. Each area maps to a different reason a business does or doesn't get cited.
What do the models actually say right now?
We query ChatGPT, Perplexity and Gemini using the questions your customers are likely to ask — category + city, service + neighbourhood, comparison questions. We document what each assistant says: are you named, are you described correctly, are you mentioned at all? This is the ground truth the rest of the audit explains.
Can models extract clean facts from your site?
We check for Organization or LocalBusiness JSON-LD schema, verify the fields are populated correctly, and confirm the facts in schema match what the rest of the site says. Missing or malformed schema is one of the most common and most easily fixed gaps we find. A model that cannot extract facts reliably will default to a competitor that makes it easy.
Does your business tell the same story everywhere?
We pull listings from Google Business Profile, Apple Maps, Yelp, Bing Places and key industry directories, then check name, address and phone for consistency. Conflicting data across sources lowers model confidence. We document every discrepancy and rank them by how many sources carry the conflict — a useful proxy for how much damage each one does to your entity clarity.
The two other layers the audit examines.
Entity and schema are the foundation. Content and reviews determine how much a model can say about you once it has identified who you are.
Content analysis: We review your key pages for answer-shaped content — direct answers to the questions buyers ask, specific service descriptions with category and location named, FAQ sections that give models liftable passages. A site full of "we are passionate about serving you" delivers nothing a model can cite. We identify the specific pages and questions where content work would have the most impact on citations.
Review signals: Review volume, recency, rating and response rate all feed AI recommendation algorithms, particularly for local queries. Models that surface businesses in response to "best [service] in [city]" weight review signals heavily. The audit documents your current position — stars, review count, last review date — relative to the competitors already appearing in AI answers for your target queries.
The output of an AI visibility audit.
The audit ends with a documented gap map, not a vague score. Each finding links to a cause and a fix.
Current state documentation
Exactly what each major AI assistant says about your business today, captured and categorized: named correctly, named incorrectly, not named, named for wrong services.
Prioritized gap list
Every gap ranked by the likely improvement in citation frequency. Structural fixes like missing schema come first; content and review work follows.
Clear next steps
A sequenced work order: what to fix this week, what to build over the next month. At TRIVOX we either hand this off as a report or execute it ourselves, depending on what you need.
Related, and what to do next.
Questions, answered.
What is an AI visibility audit?
An AI visibility audit systematically checks what generative AI assistants — ChatGPT, Perplexity, Gemini, Google AI Overviews — say about your business today. It also reviews the underlying signals that drive those answers: entity data, schema markup, directory consistency and content structure. The output is a prioritized gap map that shows what to fix and in what order.
How is an AI visibility audit different from an SEO audit?
A traditional SEO audit focuses on page rankings, backlinks, crawlability and keyword optimization. An AI visibility audit focuses on the signals generative models use to form recommendations: entity clarity, schema data, NAP consistency across directories, answer-shaped content and review signals. The two overlap at the technical foundation but diverge significantly from there.
How often should a business run an AI visibility audit?
A baseline audit before any GEO work is essential. After that, a light check every three to four months is reasonable — AI models update their training data and re-crawl regularly, so improvements you make can show up relatively quickly, and new competitors can displace you just as fast. Annual audits are the minimum.
Can I run an AI visibility audit myself?
Partially. You can query ChatGPT, Perplexity and Gemini directly with category and city questions to see whether you appear. You can check your own schema with Google's Rich Results Test. You can compare your GBP listing to your website. What is harder to do manually is a full directory audit across dozens of sources and a structured gap prioritization. That's where a professional audit adds the most time savings.
What are the most common gaps an AI visibility audit finds?
In our experience, the most common gaps are: no Organization or LocalBusiness schema on the website, inconsistent business name across directory listings, a Google Business Profile that is incomplete or has thin content, service pages that never name the service category plainly, and no answer-shaped content that models can quote. Most businesses have at least three of these five.
How long does an AI visibility audit take?
A focused audit typically takes one to two weeks from kick-off to gap report. That includes live assistant testing, schema review, directory scraping and comparison, content analysis and review signal documentation. Businesses with dozens of locations or very broad service offerings take longer.