Learn/AI visibility checker

AI visibility checker: run it yourself in 20 minutes (2026)

To check your AI visibility, ask four buyer-shaped questions across ChatGPT, Perplexity, Gemini, Claude and Grok, then record two things for each: whether your brand is named in the answer text, and whether your domain appears in the citation row. The gap between those two columns is your actual problem.

You do not need a tool for the first check, and running it by hand once will teach you more than a month of dashboards. The only mistake that matters is the one almost everyone makes on their first attempt.

Why does checking your brand name give the wrong answer?

Because nobody researching a purchase types a brand they have already chosen. Asking an engine about your own company returns a description, not a recommendation, and it will almost always be flattering. It tells you the engine knows you exist, which was never the question.

The question that decides whether you get the customer is the one your buyer actually types, and that question describes a situation rather than a company.

This is not a quirk of one engine. Google's own documentation describes AI features selecting from pages that answer the query rather than pages about a brand, and OpenAI's crawler docs make clear the same retrieval step runs before an answer is written. The engine searches the question first. Your brand name never enters the process.

What are the four prompts to run in 2026?

Run a category prompt, a comparison prompt, a safety prompt and a how-to prompt across five engines. Between them they cover the four moments a buyer reaches for an answer engine. Swap the bracketed parts for your own category and the situation your best customers are in.

CATEGORY best [product type] for [buyer situation]
COMPARISON [you] vs [competitor], which is better for [use case]
SAFETY is [product type] safe, what should I check first
HOW-TO how do I [the job your product does]

Four prompts across five engines is twenty results, and three pulls each because these systems are not deterministic. That is the twenty minutes.

Start with ChatGPT, Perplexity and Gemini, which carry most buyer research. Add Claude if you sell to technical or enterprise buyers, and Grok if your category lives on X. Both are worth checking even at low volume, because they fail differently: Claude hedges when your trust footprint is thin, and Grok swings with whatever is being discussed that week.

CATEGORY “Best AI visibility tools 2026” COMPARISON “Profound vs Otterly.AI” SAFETY “Are AI visibility tools accurate?” HOW-TO “How do I track AI mentions?” ChatGPT Perplexity Gemini Claude Grok WHO IS NAMED IN THE PROSE This is the recommendation. WHO IS CITED UNDERNEATH Pages you need to be on.
Four prompt shapes, five engines, and the two columns that decide what you do next.
EngineWhy it mattersShows citations?Watch out for
ChatGPTLargest share of buyer researchYesAnswers vary most between pulls. Run it three times.
PerplexityMost transparent sourcingYesFavours recent pages, so a fresh competitor post can distort one pull.
GeminiGrounds in Google's indexPartialBlocked entirely if Google-Extended is disallowed in your robots.txt.
ClaudeSkews technical and B2B buyersYesConservative on unverified claims, so a thin trust footprint shows up here first.
GrokWeighted toward live X discussionPartialMoves fastest and reverts fastest. Useful as a sentiment read, poor as a baseline.

How do you read the results in 2026?

Split every answer into two columns. Left column: which brands are named in the prose. Right column: which domains appear in the citation row underneath. Being in the right column only means you supplied the research while somebody else got recommended.

  1. Record who is named, not who is citedRead the answer text first and write down every brand the engine names. This is the recommendation, and it is what a buyer acts on.
  2. Then read the citation rowThose domains are your target list. In our corpus of 4,969 citation events, 89% pointed at a domain the brand does not own, so most of that row is pages you cannot edit.
  3. Run each prompt three timesAnswer engines are not deterministic. One pull is an anecdote. Three tells you whether a result is representative.
  4. Note which sources repeatA domain that appears across several prompts is carrying disproportionate weight in your category. That is where the next piece of work goes.
What the result means

Named and cited. You are in good shape. Keep the pages that earned it fresh and monitor for slippage.

Cited but not named. The most common outcome and the most expensive. Your content is good enough to source and your entity is not strong enough to recommend. Work on third-party presence and category positioning rather than more blog posts.

Neither. You are not in the candidate set at all. Find the roundups that rank for your category question and get onto them. That is the whole job until it changes.

Named and cited The model recommends you and points to a page you control. Cited, not named Your page is the evidence, someone else is the answer. Neither You are absent from both the prose and the sources beneath it.
Three outcomes, three different pieces of work. Most brands land in the middle one.

When is it worth automating this?

Automate once you need the answer to be comparable month over month. Doing it by hand tells you where you stand today. It cannot tell you whether last quarter's work moved anything, because you will not have run identical prompts under identical conditions.

The manual check has one real weakness. Answer engines sample, so your twenty results are a snapshot taken under conditions you cannot reproduce next month. That is fine for a diagnosis and useless for measuring progress.

This is the point where a tool earns its cost, and there are several good ones. We compare eight of them, including our own with the conflict disclosed, in the best AI visibility tools guide.

citeOS is the crypto-specific option. It runs 20 buyer-shaped prompts across five engines for 100 observation points per audit, scores mention at 0.60 against citation at 0.20 because being named is worth roughly three times being cited, and publishes every weight in the methodology. If you are not in crypto, one of the horizontal tools will serve you better and the guide above says which.

The free scan runs the same check automatically across all five engines and takes about a minute. No signup, no card.

Run the same check across five engines, automatically.

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Frequently asked questions

What is an AI visibility checker?

A way of testing whether AI answer engines name your brand when someone asks a buying question in your category. It can be a paid tool or, as described here, four prompts run by hand across three engines in about twenty minutes.

How often should I check AI visibility?

Monthly is enough for most brands. Answer engines move faster than that, but the underlying sources they draw on move slowly, so weekly checking mostly measures noise unless you have just shipped something.

Why do I get a different answer every time I ask?

Answer engines sample rather than look up, so identical prompts return varying responses. Run each prompt three times and treat the pattern as the signal. A brand named in two of three pulls is genuinely in the candidate set.

Does my website affect whether AI recommends me?

Less than most teams expect. Your site needs to be crawlable and clearly written, but in our own measurement 89% of citations pointed at domains the brand does not own. Third-party pages carry most of the weight.

Can I check AI visibility for free?

Yes. The method on this page costs nothing but your time. citeOS also runs a free scan across five engines if you want the same diagnosis produced automatically.

Sagar Saxena · Founder, Emergence Media

Growth marketer working in Web3 since its early days, and the person who built citeOS. The figures on this page come from our own measurement work: 4,969 AI citation events across 10 audited crypto brands, 5 engines and 979 source domains.

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