What does AI say about you?
Your buyers ask AI about you before they ever reach your site. This checks how Claude, ChatGPT, Gemini, and Perplexity actually describe your brand right now. Using model APIs with live web search or grounding where the engine supports it to approximate how the assistants answer, it scores your visibility and cross-engine consistency and flags coverage gaps and possible accuracy risks (where the engines disagree or hedge) for you to confirm. It reads the engines against each other, not against a verified fact set.
What is AI Brand Visibility Checker?
The AI Brand Visibility Checker is a free browser tool that asks leading AI engines like Claude, ChatGPT, Gemini, and Perplexity how they describe your brand, then scores how visible and consistent that picture is and flags coverage gaps plus possible accuracy risks where the engines disagree or hedge. It reads the engines against each other, not against a verified fact set, so the flags are things to confirm.
It runs on your own API keys and doesn't store them.
How it works
You enter your brand name (and optionally your domain), add an API key for each engine you want to query, and the tool calls each model's API, using live web search or grounding where the engine supports it, to approximate how the assistant would answer. One AI model then reads all the answers side by side to produce a visibility and cross-engine consistency score, surface coverage gaps and possible accuracy risks (flagged where the answers conflict or hedge, not verified against ground truth), and suggest how to fix them.
Who it’s for
For founders, marketers, and SEO/GEO teams who need to know what AI assistants tell buyers about them before those buyers ever visit the site; the outcome is a clear, quotable read on how consistent and complete your AI presence is, plus a concrete fix list so you can close coverage gaps and correct the shaky facts the engines surface.
In practice
A B2B software company enters its name and domain, adds Claude, OpenAI, and Gemini keys, and sees that two engines describe it with an outdated product focus and one states a different headquarters city, the report scores its visibility and consistency, flags those as possible accuracy risks to confirm, and lists the pages and facts to publish so the engines describe it correctly.
Ask every engine you have a key for.
Enter your brand, add an API key for each engine you want to check, and see exactly how each one talks about you, side by side, with one AI model reading all four answers to score cross-engine consistency and flag possible accuracy risks, answers are not verified against a ground-truth fact set. (A Claude, OpenAI, or Gemini key is required for the visibility score, gaps, and fix plan; a Perplexity key alone returns only its own answer.)
Use your own API keys : used once for your request, never stored on our servers
Keys travel over HTTPS to our server, which calls each AI provider for this one request, then discards them, we don't log or store your keys. Your brand and the engines' answers are processed by the AI providers under their own data policies. NYFTY doesn't charge for this tool, but your AI providers may bill you for the API usage on your own accounts. Want a full audit done for you? Talk to us.
The AI Visibility Method, written down.
AI visibility is the share of generated answers to buyer-intent questions in which a brand is named, and the share in which its own domain is cited as a source. Those two numbers are not the same, and conflating them is the most common measurement error we see. Here is the method, published so anyone can reproduce or challenge it. The baseline below was collected with a scheduled script rather than with the tool above, the tool enables web search on every engine, the baseline script did not, and that difference is stated where it matters.
- Fix the prompt set. Write the buyer-intent questions a real prospect would ask an assistant, category questions, not your brand name. Freeze the wording so runs stay comparable.
- Query each engine separately. Send the identical prompt set to each engine you care about. Never average engines together; they retrieve differently and behave differently.
- Score presence and citation apart. Record two distinct outcomes: whether the brand is NAMED in the answer, and whether the brand's domain appears as a linked source. These are different problems with different fixes.
- Re-run on a fixed cadence. Generated answers vary between runs, so a single check is an anecdote. Measure rates over repeated runs on a schedule and report the date, the count, and the prompt set with every number.
Named is not cited.
A brand can be recommended in an answer while no link to it appears, and a domain can be cited as a source for a claim without the brand being recommended at all. Being named is decided when the answer is generated; being cited is decided when sources are retrieved. Being named without being cited usually means the model learned the brand in training but nothing current corroborates it. Being cited without being named usually means the page is useful reference material but the brand is not positioned as a solution.
How each engine actually reaches a source.
These behaviors differ enough that one score across all engines hides the problem:
| Engine | How it reaches a page | What that means for you |
|---|---|---|
| ChatGPT | The base model does not retrieve. With web search enabled it can fetch pages reachable by its search crawler. | If your robots rules block that crawler, you are ineligible before ranking matters. |
| Claude (not measured in this baseline) | No live index. It only sees a site if a search tool is invoked during the conversation. | It favors self-contained, attributable claims; a named method, or a number carrying its date and method. |
| Gemini | Grounded against Google's own index and surfaces. | Classic Google visibility and entity clarity carry over here more directly than elsewhere. |
| Perplexity | Search-first: it retrieves, then answers with visible citations. | Search-first, so a change in what is retrievable can surface sooner. We have not measured comparative latency across engines. |
No engine publishes its citation-selection weights. The descriptions above reflect each provider's public documentation plus, where noted, the engines' own self-descriptions; which we have not independently verified. Claude was not measured in this baseline.
Our own baseline, published.
We measured ourselves first, and we publish the result even though it is unflattering. As of 2026-07-24, across 18 observations (6 buyer-intent prompts × 3 engines; ChatGPT (GPT-5.6), Gemini 3.1 Pro, and Perplexity Sonar), NYFTY Labs was named in 1 of 18 answers, and in 0 of the 15 answers to prompts that did not contain our name. The one mention came from a prompt that already named us, which is the least interesting kind of hit. Citations: 0 of 6.
| Measure | Result | Method |
|---|---|---|
| Observations | 18 | 6 fixed prompts × 3 engines, single run, 2026-07-24 |
| Unprompted brand presence | 0 of 15 | The 3 cells from the one brand-naming prompt are excluded |
| Domain cited as a source | 0 of 6 | Only the web-connected engine could return sources; the other two were queried without web search, so citation was not measurable there |
Limitations, stated plainly: this is one brand, one run, one date; a baseline, not an industry benchmark. Generated answers vary between runs, so treat any single figure as a starting point and judge movement by the trend. We re-run this on a schedule and will publish the multi-run dataset as it accumulates.
What we think moves the number.
This is a hypothesis drawn from one negative observation, not a controlled test; we are stating it so it can be checked against what we publish next. Our site was already technically complete, valid structured data, a published llms.txt, crawlable to every major AI crawler, sub-second server response, and it still scored one mention and zero citations. Markup makes a page eligible and interpretable; it does not make a page worth citing. What changes the number is corroboration somewhere other than your own domain: independent sources that reference you, plus original material of your own that is specific enough to quote. If a vendor tells you a schema change will get you cited, ask them for their measured before and after.
See how we work on this → · Measure citation share → · Track it over time → · Read the full guide →
Questions, answered.
Yes, the tool itself is free to use. Because it is BYOK, any model/API usage costs are handled through your own provider account.
Yes, this is a bring-your-own-key tool. Your API key is sent over HTTPS to NYFTY's server only to make the request(s) for that single run, is never logged or stored, and is discarded immediately after.
It checks the AI engines and models shown in the tool for your run. The exact coverage can change as model providers update access and as NYFTY adds or removes supported integrations.
It shows how AI engines describe your brand, a visibility and cross-engine consistency score, where your brand is missing or weak, possible accuracy risks it flags where the engines disagree or hedge, and recommended fixes. It reads the engines against each other, not against a verified fact set, so treat the flags as things to confirm, then use them to improve source pages, FAQs, bios, product descriptions, third-party listings, and other content AI systems may rely on.
Being named means the AI answer mentions your brand. Being cited means the answer also points to a source or reference for the claim, which can be your site or another source; neither outcome is guaranteed.
AI answers can vary because models are probabilistic, prompts may be interpreted differently, provider systems change, and the web or retrieval sources may update. Treat the report as a directional audit, not a complete or permanent record of your brand visibility.