AI Visibility Checker: See Which Brands AI Names Instead of Yours

AI brand visibility is how often an AI assistant names your brand when it answers a question in your category. It is not a ranking and it has no position number. When someone asks an assistant which tool to buy or which supplier to use, the assistant returns a short list of names, and those names collect the attention that used to go to the top three links.

Share of voice turns that into a comparable figure. If ten brands are named across the answers in your space and yours accounts for a quarter of the mentions, you hold roughly a quarter of the available attention.

The two can diverge sharply from classic search. A brand can rank well organically and stay almost invisible in AI answers, usually because the sources assistants draw from never mention it.

The checker runs two lookups against SEOmator's own LLM Mentions dataset and returns three things: your mention count on Google AI Overview, your mention count on ChatGPT, and a ranked table of the brands named alongside you.

Share is each brand's mentions divided by the total across the brands in the table. That makes it a share of the visible competitive set, not of every AI answer ever produced.

Two corrections run before the percentages are calculated, both of which came out of testing the raw data rather than the documentation:

Neither correction is cosmetic. Both change the headline number, as the next section shows.

We built this tool in July 2026 and ran it against our own domain and a set of well-known SaaS brands while testing. Three things showed up in the raw responses that the finished checker now corrects for. These are single test runs rather than a large study, so treat them as observations about the data source, not as market research.

Roughly a quarter of the returned "brands" are not brands

On a semrush lookup, four of the nine entities returned were descriptions rather than companies: SEO platform, SEO software company, American singer-songwriter, and Portuguese footballer. Together they accounted for about 27% of the mention total.

Left in place, they inflate the denominator and understate every real company. Filtering them moved SEMrush's share from 25.1% to 34.4% on the identical query, a swing of more than nine points. The dataset offers no field to filter on, so the checker separates the two by capitalisation: descriptive phrases leave later words lowercase, while real multi-word brands capitalise them.

The same company comes back under two spellings

SEMrush and Semrush arrive as separate rows with separate mention counts. Any tool that reports the raw rows will show one company twice, each time at roughly half its real weight, and will rank it below competitors it actually leads.

A brand can have thousands of mentions and still be absent from the competitive set

We ran our own domain and got an uncomfortable result. seomator returned 98 mentions in Google AI Overview answers and 21 in ChatGPT, yet SEOmator did not appear in the ChatGPT brand set for its own category at all. Ahrefs, SEMrush, and SEObility did.

The brands that did appear were each named once or twice, so this is a thin slice rather than a verdict. It still illustrates the point the tool exists to make: mention volume and competitive presence are different measurements, and a healthy-looking total can hide absence from the conversation that decides purchases.

Because that is the only market the brand-level data covers, and we confirmed it directly rather than taking the documentation's word for it.

Requesting the same co-mention data with the platform set to Google AI Overview returns zero rows. Not sparse results, zero, for domain and keyword lookups alike. Brand entities are extracted only from ChatGPT answers, and that extraction is published for the US/English market alone.

This is why the checker has no country or language selector, unlike our other market-aware tools. A picker that could only ever return an empty table would be worse than no picker. Mention totals are shown for both platforms, since those exist at the total level, and the results state the scope in plain text.

If your market is not US/English, read the competitive table as the English-language conversation about your category. That is frequently still the conversation shaping how models describe your space.

The two numbers answer different questions, and reading only the first is the most common mistake.

Mentions counts how often the brand was named. It measures presence.

AI search volume estimates how much question volume sits behind the answers where the brand appears. It measures importance.

The second row is the one worth hunting for. It is often a stronger commercial position than the first, and it disappears entirely if you rank brands by mention count alone.

It means assistants are answering questions in your category without naming you. That is a real finding, and it is the most actionable output this tool produces.

Check the input before drawing conclusions. Enter the brand name rather than a domain, since the data indexes brands by name. Try the category term your customers would actually type, because answers are written in the language of the question, not the language of your product page.

If the brand is genuinely absent, the table still earns its keep: the brands listed are your competitive set as assistants currently perceive it. In our experience that list rarely matches the one you would have written down, and the unexpected names are the useful part.

Assistants build answers from sources, so mentions follow from being present and quotable in the material they draw on. Three things move the number, roughly in order of effort.

Be present on the sources that get cited. If the pages cited for your category never mention you, on-site work will not put you in the answer. Identify those pages and get accurately described on them.

Answer the underlying question directly. Content that states its answer near the top, in plain language, is easier for a model to lift and attribute than content that circles the topic first. State the claim, then the context.

Stay machine-readable. Crawlers have to read you cleanly before any of this applies. Our GEO audit tool checks AI crawler access, structured data, and passage structure together, and the free llms.txt generator produces the file. You can also check a specific query with the Google AI Mode checker.

Four limits worth knowing before you act on the numbers.

The data is aggregated, so it lags. It will not reflect a campaign that launched last week, and it is not a real-time monitor.

The competitor table covers ChatGPT for US/English, as described above. It is not every assistant in every language, and we would rather say so than present a partial dataset as a global one.

Share is calculated across the brands displayed, so a longer or shorter table changes every percentage. Compare like with like when you track it over time.

Mentions are not conversions. Being named in an answer is the start of the journey. What happens next still depends on everything that has always mattered.