Competitor AI visibility can be measured. The method is simple enough to run by hand before buying any tool. And the data is sharp enough to shape a real GEO plan.
Most teams start this work after a prompt. A prospect mentions a competitor appeared in ChatGPT. A founder checks Perplexity and finds a rival mentioned in the category answer.
A search for the brand's own category turns up no brand at all. That is a poor place to start. A planned audit is better.
Key takeaways
- A competitor audit needs only the engines, a set of questions, and a spreadsheet. One afternoon of work shows where each rival stands.
- Track four signals per competitor: mention rate, share of voice per engine, average position, and citation share.
- Competitors that show up on every engine have strong entity signals and wide third-party coverage. Those that show up on one engine but not others have tuned for that one channel.
- The most useful output is not a ranking. It is a map of the questions competitors own that the brand does not, and why they own them.
What should a competitor AI visibility audit measure?
The audit tracks four signals per brand: mention rate, average position, share of voice per engine, and citation share. Together they show how often a rival appears and where it lands in the answer. They also show which engines favor it and which sources back each mention.
| Signal | What it measures |
|---|---|
| Mention rate | How often the rival appears in answers to the question set |
| Average position | Where it lands in the answer: first, second, or last |
| Share of voice per engine | How its showing differs across ChatGPT, Gemini, and Perplexity |
| Citation share | Which outside sources the engine draws on when it mentions the rival |
A rival that shows up in seventeen of twenty questions has a very different reach than one that shows up in four. Position matters because people read AI answers like prose. The brand mentioned first gets the most attention.
Share of voice varies because ChatGPT, Gemini, and Perplexity share only a small fraction of their cited domains. A rival strong on Perplexity but absent from ChatGPT has a clear profile: real SEO but weak entity signals. That points to a clear fix.
Review sites, analyst reports, press, and the rival's own content each point to a different kind of spend. Citation share shows which one is paying off.
How should the question set be built?
A good question set has three layers, shown in the table below: category, use case, and comparison.
Twenty to thirty questions across these layers give a clear picture without eating a week.
| Layer | Example questions | What it shows |
|---|---|---|
| Category | "What is the best [category] tool?" | What buyers ask first, and which brand is the default |
| Use case | "Best [category] for agencies" | Where rivals own a niche |
| Comparison | "[Competitor A] vs [Competitor B]" | How engines frame the field, at the highest intent |
How is the audit run?
Run each question on ChatGPT, Gemini, and Perplexity. Log each answer in a spreadsheet: the question, the engine, the brands mentioned in order, and the cited sources. A summary tab rolls up mention rate, average position, and share of voice per brand per engine.
Each engine gets its own tab. The summary tab shows where each rival is strong and where it is weak.
Run the audit monthly and the snapshot becomes a trend line. A shift in a rival's mention rate or position is the first sign it is spending more on GEO, or losing ground.
What does the audit data reveal about competitors?
Rivals that show up on every engine have strong entity signals and wide third-party coverage. Rivals strong on Perplexity but absent from ChatGPT often have good SEO but weak entity signals. Rivals strong on ChatGPT but absent from Perplexity often have a strong place in training data but old content that live search skips.
| Pattern | Likely cause | Fix |
|---|---|---|
| Strong on all engines | Strong entity signals and wide coverage | Keep the basics strong |
| Strong on Perplexity, weak on ChatGPT | Good SEO, weak entity signals | Entity work and earned citations |
| Strong on ChatGPT, weak on Perplexity | Strong in training data, old content | Fresh content for the same questions |
The output that matters most is not a ranking. It is a list of the category questions a rival owns that the brand does not.
Add a best guess at what the rival did to own them. That guess drives where the GEO budget goes.
To run the same audit on the brand's own visibility first, see the AI visibility baseline guide.
Closing the gaps usually starts with content. How to write content that AI engines cite covers what to change on the page.
Zumi is an AI Search Intelligence Platform that runs this same audit on a schedule, per engine, instead of by hand once a month. Book a demo to see a real competitor set broken down by the four signals.