AI Visibility Checker
Ask ChatGPT, Claude and Gemini the same question several times and count how often your brand comes back. You get a frequency with a confidence interval, not a rank.
How this AI visibility checker works
Type your brand, type the questions your buyers actually ask, and the tool sends each question to ChatGPT, Claude and Gemini several times over. It then counts how many of those answers mention you. That count is the whole product. There is no score to interpret and nothing is weighted behind your back.
Before any of that runs, it checks whether the AI crawlers can reach your site at all. Both layers matter and they fail for different reasons, so the tool reports them separately instead of folding them into one number.
Why a rank number is the wrong answer
Most AI visibility tools hand you a position. Rank 3 for ChatGPT, rank 7 for Perplexity. The problem is that language models are not deterministic: ask the identical question twice and you often get two different lists, in a different order, naming different companies. A single position is one sample dressed up as a measurement.
Run the same prompt ten times and the honest answer looks different. You were named in six of ten runs. That is 60 percent, and with ten runs the true rate could plausibly sit anywhere between 31 and 83 percent. Both halves of that sentence are useful. The rank was never useful.
So the headline here is a frequency and a Wilson 95 percent confidence interval, shown as a pale band behind the solid arc. The band shrinks when you run more samples, which is exactly the lesson most tools hide.
Training memory and live retrieval are two different problems
A model can name you for two completely unrelated reasons. Either you are in its weights from training, or it searched the web while answering and found you. Those have different failure modes and different fixes, so the tool asks each question twice: once with web search off, once with it forced on.
| Web off | Web on | What it means | Where to start |
|---|---|---|---|
| named | named | You are in the model's memory and in its search results. The stable position. | Nothing urgent. Keep publishing. |
| not named | named | Retrieval only. Fragile: lose crawler access and you vanish the same day. | Third-party mentions, entity markup, schema. |
| named | not named | The model remembers you but cannot find your live pages. | Crawler access, JS rendering. |
| not named | not named | Invisible in this market for this question. | Retrieval first. It moves in weeks, training takes a year. |
The retrieval half costs roughly eight times what the training half costs, because the search itself is what you pay for, not the tokens. That is why the free check measures training memory only and the retrieval column needs your own DataForSEO key. It is the point where real money starts, said plainly rather than buried in a pricing page.
Perplexity sits in only one column, on purpose
Perplexity has no offline mode. It is a search product and it searches every single time, so there is no training-memory reading to take from it. Rather than show you an empty cell, the tool leaves Perplexity out of the free check and includes it in the retrieval pass, where its answers carry more citations than any other engine.
Where the AI actually looks
The runs above ask the models a question. That tells you what they say. It doesn't tell you which sources they lean on while answering, and those are two different problems with two different fixes.
So the tool also reads the archive. For a category keyword it pulls the domains that actually get cited in real Google AI Overview answers in your market, ranked by how often, with the AI search volume behind each. Run it on "seo tool" in Germany and YouTube comes first with over three thousand citations, ahead of every SEO vendor. Nobody would have guessed that, and you only find it by looking.
Your own domain is marked in the list when it's there. When it isn't, the list is still the answer: those are the pages answering your category in your place. The check costs a credit of its own, because the call behind it runs about a hundred times the price of a model call. And it reads archived answers rather than running a live test. ChatGPT's mentions database takes United States and English only, which we measured rather than read, so everywhere else this is Google AI Overview.
What the crawler gate actually gates
Blocking an AI crawler in robots.txt does something, but not the thing most articles claim. The bots split into two jobs and only one of them changes today's answer.
GPTBot, ClaudeBot and Google-Extended collect training data. Block them and you slowly fall out of future model releases, which is a problem measured in quarters. OAI-SearchBot, Claude-SearchBot, PerplexityBot and Google-Agent fetch pages while the model is answering a live question. Block those and you disappear from the retrieval column immediately.
So the gate reports both, tells you which side each bot is on, and checks the real server response as well as the robots rule. A bot that is allowed in robots.txt and gets a 403 from your firewall is still blocked, and that mismatch is common enough to be worth a separate column.
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