AI Visibility Audit — Does ChatGPT Recommend Your Brand?

You know your Google ranking. But do you know whether ChatGPT, Perplexity and Google AI Overviews recommend your brand — or your competitors? More and more buying decisions start with a question asked to AI, not a phrase typed into a search box. An AI visibility audit (a GEO audit) answers the question: do you even exist in generative answers, who cites you, and how do you compare to competitors? This guide explains how to run one — including the new reports that arrived in 2026.

You know your Google ranking — but do ChatGPT, Perplexity and AI Overviews recommend you or your competitors? An AI visibility audit (GEO) measures AI Share of Voice, citations and sentiment. I explain how to run one step by step.

What changed in 2026: the first official data

Until recently there was no platform data on AI visibility at all. That just changed — but only partly:

• Google Search Console — the "Generative AI" report (June 2026): a dedicated breakdown of impressions in AI Overviews and AI Mode, by page, country and date. Important limitation: it's impressions only — no clicks, CTR or query data. Google stresses this data was always included in the overall Performance report totals; the new part is the isolated view. An opt-out toggle to exclude content from AI features also arrived.
• Bing Webmaster Tools — the "AI Performance" report (preview since February 2026, expanded in June): shows when your site is cited in Microsoft Copilot answers and Bing AI summaries — citation counts per URL, "grounding queries" (the phrases the AI used to retrieve content), and since June also intents, topics, citation share and comparisons.

Still, the picture is incomplete: ChatGPT and Perplexity provide no first-party analytics, and Google's data is impressions only. So a GEO audit still requires actively querying the engines.

What an AI visibility audit measures

The heart of the audit is AI Share of Voice — the share of AI answers (across a defined set of your customers' questions) in which the brand is mentioned, cited or recommended, relative to competitors.

How to run an audit — step by step

1. 1.Build a question set. 30–100 real customer questions (problem, comparison, "best X for Y", local, branded).
2. 2.Query the key engines with the same questions: ChatGPT (with search), Perplexity, Google AI Overviews and AI Mode, Copilot, Gemini.
3. 3.Sample repeatedly. Model answers are non-deterministic — the same question yields different answers. Ask several times to average out.
4. 4.Record results: was the brand mentioned, was it cited (and which page), sentiment, are the facts correct.
5. 5.Compute Share of Voice against 2–3 competitors.
6. 6.Diagnose gaps — where competitors are cited and you aren't, and why.

How to build a good question set

An audit is only as good as the questions you ask. A set of 30–100 questions should mirror the customer's real decision journey, not your SEO keywords. Six categories worth covering:

The rule: ask in the customer's language, in full sentences, the way people actually talk to AI — not keywords. The closer to real buying intent, the more useful the audit.

Where engines get sources — and why Bing is key

This is the audit's most important technical takeaway. Engines pull from different indexes:

• Google AI Overviews and AI Mode → Google's index (RAG over the normal index, with "query fan-out").
• ChatGPT and Microsoft Copilot → largely the Bing index. Analyses of ChatGPT citations show ~87% overlap with Bing's results.
• Perplexity → a hybrid model: its own crawler (PerplexityBot) plus search-engine APIs.

The takeaway: you can't do GEO while ignoring Bing. Being indexed and clean in Bing Webmaster Tools unlocks ChatGPT and Copilot simultaneously — yet most companies focus only on Google.

How to read the results — from raw data to a plan

Raw "mentioned / not mentioned" is just the start. The audit's value lies in prioritizing gaps:

• High-value gaps — questions close to the buying decision (comparisons, "best X") where competitors are cited and you aren't. Direct your effort here first.
• Easy-to-close gaps — questions you have content for but aren't cited on (often a technical problem: no Bing indexation, blocked bots, poor extractability).
• Factual errors — AI says something untrue about you. These are urgent, because they damage your image on every query.

A practical matrix: X axis = question value (how close to a decision), Y axis = difficulty of closing the gap. Start in the "high value / low difficulty" quadrant — quick wins, which are most often technical in nature.

Reverse-engineering competitors — why AI cites them, not you

When the audit shows a competitor cited for a given question, ask the simple question: why? The most common reasons, from easiest to fix:

• They're in the Bing index and you aren't — the most common and cheapest gap to close.
• They have extractable content — a direct answer, data, a table the model can "lift"; you have filler.
• They're mentioned externally — Reddit, media, rankings, Wikipedia; their entity is more strongly corroborated.
• They have fresher content — the model prefers current sources for date-sensitive questions.

Open the competitor's cited page and read it the way a model would: does it answer directly? Does it have numbers and quotes? Is it current? That's your to-do list.

Brand hallucinations — how to detect and correct them

The most dangerous audit result isn't "not cited" — it's AI saying something untrue about you: a made-up offer, a wrong price, a non-existent review, confusion with another company. The fix mechanism:

• Unify your own source of truth — a consistent, unambiguous "About" page and structured data; models learn from what you publish.
• Strengthen external corroboration — the more credible, consistent sources, the lower the chance of a hallucination.
• Monitor after the fix — re-run the same questions after a few weeks; models update their "understanding" of a brand with a lag.

Tools for tracking at scale

A whole category of AI Share of Voice tools has emerged: Profound, Otterly, Peec AI, AthenaHQ, Scrunch (acquired by Sitecore in 2026), plus modules in classic SEO tools — Semrush AI Visibility Toolkit and Ahrefs Brand Radar. They differ in methodology: some generate synthetic prompts (Semrush), some rely on real search-derived queries (Ahrefs). Prices, selection criteria and the "build your own" variant are compared in a separate post. On top of that come first-party reports: Bing AI Performance and the Generative AI report in GSC.

From audit to improvement

An audit is the diagnosis — the treatment is GEO. The most common findings and actions: open the site to AI bots (GPTBot/OAI-SearchBot, PerplexityBot, Bingbot) and ensure indexing in Bing too, rewrite key content for citability, build a consistent entity and earn third-party corroboration. Broader context: SEO vs GEO, getting cited in ChatGPT and Perplexity/SearchGPT.

A minimal one-day audit — where to start

You don't need to buy a several-hundred-dollar-a-month tool right away. You can run a first, manual audit in a single day:

1. 1.List 20 questions your customers actually ask (problem, comparison, "best X for Y", branded).
2. 2.Ask them in ChatGPT (with search), Perplexity and Google AI Mode — each question 2–3 times, because answers are non-deterministic.
3. 3.Tally in a spreadsheet: was your brand mentioned, was it cited (and which page), which competitors appeared, what was the sentiment.
4. 4.Compute a simple Share of Voice: in how many of the 20 questions you appeared vs competitors.

This quick pass shows whether you exist in AI answers at all and where competitors are beating you — enough to set priorities before you reach for scale monitoring tools.

---

I run AI visibility audits (GEO) as part of full AI-GEO — with a concrete improvement plan. I teach it in the SEO & GEO course. Get in touch — I'll check whether AI recommends you or your competitors.

Worth reading next:

• How to measure Share of Voice in AI
• How to measure traffic from ChatGPT and Perplexity in GA4
• Ranking in Perplexity and SearchGPT