AI prompt — “which providers should I shortlist?”
An AI visibility audit measures whether, and how, your brand shows up when customers ask AI engines for recommendations. It’s the new equivalent of checking your search rankings, except the “ranking” is a citation inside a generated answer.
Right now, ChatGPT, Gemini, Perplexity, and Google’s AI Overviews are answering questions about your category, comparing options, and making recommendations: drawing on sources you’ve probably never reviewed. Most brands have no idea whether they’re being cited, ignored, or misrepresented. An AI visibility audit replaces that blind spot with a clear picture.
Here’s what it is, why it matters, and a step-by-step way to run one.
What is an AI visibility audit?
An AI visibility audit is a structured assessment of how often, and how accurately, AI engines surface your brand in their answers, benchmarked against competitors. Where a traditional SEO audit checks rankings, crawl health, and backlinks, an AI visibility audit checks something newer: across a set of real buyer prompts, which brands get named, which get ignored, and whether what the AI says about you is even correct.
Why you need one
Because rank tracking is now blind to where a growing share of decisions are made. A link cited inside an AI Overview earns roughly a 1% click-through (Pew Research, 2025), so being named is the visibility, even without a visit. If a competitor is the brand the AI keeps recommending and you’re absent, you’re losing influence at the exact moment of decision, and your analytics won’t show it as a lost click because the click never existed.
How to check if ChatGPT mentions your brand
Start by asking the engines the questions your buyers ask. Build a list of real prompts (category questions (“best [product] for [use case]”), comparison questions (“X vs Y”), and problem questions (“how do I solve [problem]”)) then run them across ChatGPT, Gemini, Perplexity, Copilot, and Google’s AI Overviews. Record whether your brand appears, in what position, with what framing, and which sources the engine cites. Run each prompt more than once: generative answers vary, so you’re looking for patterns, not a single snapshot.
What to measure: share of citation
The headline metric is share of citation: the percentage of your buyer prompts where your brand appears in the AI answer, benchmarked against named competitors. Alongside it, track sentiment and accuracy (is the mention positive, neutral, or wrong?), which sources the engines pull from (so you know whose authority to earn), and how it trends over time as you make changes. Share of citation is to AI search what rank tracking was to classic SEO.
A 7-step AI visibility audit framework
- Define your prompt set. 30–100 real buyer questions across the funnel, branded and non-branded.
- Pick your engines. ChatGPT, Gemini, Perplexity, Copilot, Google AI Overviews — wherever your buyers research.
- Run and record. Capture appearances, position, framing, and cited sources; repeat prompts to catch variance.
- Benchmark competitors. Score your share of citation against named rivals, prompt by prompt.
- Diagnose the gaps. Where you’re absent, find why — thin topical depth, weak entity signals, missing structure, or no third-party citations.
- Check accuracy. Flag anywhere the AI misrepresents your brand, pricing, or offering.
- Prioritize the roadmap. Rank fixes by the buyer prompts they win, not by effort.
You can’t fix what you can’t see. The first audit is rarely flattering — but it’s the only way to stop guessing about a channel that’s already shaping demand.
Tools you can use
You can start manually: a spreadsheet of prompts and the engines themselves cost nothing and reveal a lot. Dedicated AI-visibility and brand-monitoring tools can scale the prompt volume and track share of citation over time. The tool matters less than the discipline: a consistent prompt set, repeated on a schedule, benchmarked against the same competitors. (For the underlying signals these audits grade, see how AI search and LLM visibility work.)
Audit on a cadence, not once: generative answers shift as models update and competitors publish. A one-time score is a snapshot; a quarterly audit is a trend line you can manage against.
Turning findings into a roadmap
An audit is only useful if it ends in a prioritized plan. Group the gaps into themes (topical depth, structured data, entity consistency, content extractability, third-party authority) and sequence them by the prompts they win and the revenue those prompts influence. That sequencing is the difference between a report that gets read once and a roadmap your team can act on Monday. It’s the core of our AI audits work.
Edge cases: zero citations, misrepresentation, regulated industries
If you have zero citations, don’t panic: start by earning topical authority in one narrow area rather than trying to win everything. If the AI is misrepresenting you (wrong pricing, outdated facts), prioritize correcting the source material and entity signals the engines rely on. And in regulated industries like finance, legal, and healthcare, accuracy auditing matters even more, because a confidently wrong AI answer about your product carries compliance risk, not just marketing risk. Related reading: GEO vs AEO vs SEO and zero-click search.
An AI visibility audit turns an invisible channel into a measurable one. Define real buyer prompts, run them across the major engines, score your share of citation against competitors, and turn the gaps into a prioritized roadmap. Do it on a cadence: being cited is the new ranking, and you can only improve what you measure.
See your share of citation in real numbers
We’ll run your brand against real buyer prompts across the major AI engines and show you exactly where the gaps are. Free, no obligation.
Sources
- Pew Research Center, “Google users are less likely to click on links when an AI summary appears,” July 22, 2025
- Gartner, “Gartner Predicts Search Engine Volume Will Drop 25% by 2026,” Feb 19, 2024
- OpenAI weekly active user figures (700M July 2025; 800M+ late 2025), as reported by Reuters and TechCrunch