Foundational guide
What is AI visibility?
AI visibility is whether, how often, and in what words an AI assistant names your brand when someone asks a question in your category. It is measured per assistant (ChatGPT, Claude, Gemini, Perplexity), per question intent, on a fixed set of questions, and it is reported before any blending into a single score. The blend is where the information dies: in a September 2026 run on one consumer brand, the same 42 questions produced a 73% presence rate on Perplexity and 18% on Claude. The blended score was 51, which was wrong for every one of the four.
This page defines the term, shows what the measurement looks like on real numbers, answers what a good score is, and explains how to check your own. It also says when measuring AI visibility is not worth your money.
By Sri · Last reviewed: September 2026
| Measure | Value | Source |
|---|---|---|
| Spread between assistants, same brand, same 42 questions | 55 points | 73% Perplexity, 18% Claude. Lynceus BrandPulse run, 2026-09-09. |
| Inflation from branded questions | 17 points | 62 across all 42 questions, 45 on the 30 unbranded. Same run. |
| Named on attribute questions vs recommendation questions | 67% vs 25% | Same run. |
| Brands in the public index | 30 | SaaS brands, mostly Google AI Overview, last run 2026-09-11. |
| Input-token reduction from deduplicating search results before the model reads them | 77% | Anthropic, Code with Claude 2026, London session on model selection. |
| This page's Google position for "ai visibility", last 28 days | 84.8 | 3,873 impressions, 1 click. Search Console, 2026-09-12. |
The definition
What is AI visibility?
AI visibility is the presence, position and framing of a brand in the answers that AI assistants generate. It has three parts, and a report that gives you only the first is not a measurement.
Presence. Does the assistant name the brand at all when asked a category question. Reported as a rate: named in 8 of 11 answers is 73%.
Position and share. Where in the answer the name appears, and how much of the answer is about you versus other brands and third-party sources. The first sentence of a recommendation is worth more than a mention in a footnote of alternatives. Share of paragraph, the fraction of the answer's sentences that reference you, is the working unit.
Source. Which URL the assistant leaned on to write the sentence about you. Your own page, a competitor's comparison page, a forum thread, an encyclopedia entry, a review site. The source decides whether you control the sentence or someone else does.
The contrast with search ranking is the point. A Google rank is one number for one query on one engine, and it is stable enough to trend. AI visibility is four engines (OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini and Perplexity), each with its own retrieval, each answering a slightly different way every time it is asked, and each drawing on a different set of sources. In the run above, Gemini named the brand in 7 of 10 answers and ChatGPT in 5 of 10 on identical questions. Nothing about the brand differed between those two numbers. The assistants did.
That is why the definition insists on per-assistant, per-intent reporting on a fixed question set. Drop any of the three qualifiers and the number stops meaning anything.
Measured, not defined
What does AI visibility look like when you measure it?
One brand, one week, 42 questions, four assistants. Twelve questions named the brand. Thirty described a need without naming anyone, the way a person who has never heard of the brand would ask.
Source: Lynceus BrandPulse run, 2026-09-09. Full write-up: one brand, four assistants, 42 answers.
| Intent | The question shape | Named |
|---|---|---|
| Attribute | what is it made of, how does it behave | 8/12 67% |
| Reputation | is it any good, what do people say | 5/8 63% |
| How to choose | what should I look for | 2/2 100% |
| Comparison | this versus that | 2/4 50% |
| Alternatives | what else is there | 2/4 50% |
| Recommendation | what should I buy | 2/8 25% |
| Use case | what is best for this situation | 1/4 25% |
Read the two tables together. The assistants know what this brand's products are made of and what people say about them. They do not reach for it when someone asks what to buy. That is a specific diagnosis, and it is not "publish more content". It is: be the source that gets cited on the questions where a purchase decision is being made.
The third number is the one vendors least like to show. Across all 42 questions the mention score was 62. Across the 30 unbranded questions it was 45. Nothing changed except which questions were counted. A report that mixes branded and unbranded questions, weighted toward branded, drifts upward every time your brand awareness improves while the number attached to acquiring a new customer stays flat.
The buyer
How does AI visibility affect the buyer?
The buyer usually does not click. The assistant's sentence about your brand is the impression, and the shortlist is compiled inside the answer. When a person asks what to buy and the answer names three brands, the fourth brand has lost that customer before a search results page ever loaded.
Two things make this different from losing a Google ranking. First, the loss is silent. There is no impression count that tells you the answer was generated and you were not in it. You only see it if you ask the question yourself, which is what a fixed question set is for.
Second, the reader is increasingly not a person. Anthropic's framing at its May 2026 developer conference was that a year earlier models worked for minutes, that most users now run agents for hours, and that the next generation "runs continuously". An agent doing market research on a task, or comparing options for a purchase, reads your site without any human having typed a query. It fetches, decides, and moves on. Whether your brand survives that pass is decided by the same three things: presence, position, source. Only now it is decided many times across one task rather than once.
The practical effect is a narrowing of the consideration set before the buyer knows a set exists. On recommendation questions in the run above, the brand was in 2 of 8 answers. Six times out of eight, a buyer asking "what should I buy" was handed a list the brand was not on.
The score
What is a good AI visibility score?
There is no good single score, because a single score blends four assistants and two kinds of questions. The honest version is a small table of rates, and the one that matters is unbranded presence on recommendation-intent questions. These are the working thresholds Lynceus uses when reading a run. They are ours; treat them as a starting point, not a standard.
| Unbranded presence, buying intent | Reading | What it usually means |
|---|---|---|
| Under 20% | Absent | The assistants have no reason to name you. Usually a source problem: nothing citable on the questions that matter. |
| 20% to 40% | Occasional | Named when the question is narrow or the category is thin. The brand exists to the assistant but is not a default. |
| 40% to 60% | Contested | Named about as often as the two or three brands you compete with. Movement here is real and worth tracking monthly. |
| Over 60% | Default | You are in most answers. The work shifts to position and framing: first sentence, accurate description, your own URL as the source. |
Three cautions before you put a number in a slide. The branded share of the question set changes the number more than anything you can do to your website: in the run above, including the 12 branded questions added 17 points. Ask any vendor what share of their prompt set names your brand. If they cannot answer, the score is not measuring what you think.
A single answer is a draw, not a measurement. Assistants vary between runs on the same question. Repeat sampling, three or more passes per question, is what turns a presence rate into a number with a range around it.
A blended score across assistants hides the assistant your buyers actually use. If your customers are on ChatGPT and your report says 62 because Perplexity likes you, the report is describing a world your buyers do not live in.
Do it yourself
How can I check my AI visibility?
You can check it yourself in an afternoon with a spreadsheet and the four assistants. The method is the same one a vendor uses; the vendor's value is doing it every month at scale with repeat sampling.
Write the question set before you look at any answer
Twenty to forty questions a buyer in your category would ask. Split them: some name your brand (recall), most do not (acquisition). Mark each with its intent: attribute, reputation, comparison, recommendation, use case. Lock the set. Every later run uses the same questions or the trend is not a trend.
Ask each question in each assistant
ChatGPT, Claude, Gemini and Perplexity at minimum. Use a fresh session per question so earlier answers do not leak into later ones.
Record the verbatim answer
Not a summary. The sentence the assistant wrote about you is the evidence; everything else is derived from it.
Score presence, position and source
Named or not. Character position of the first mention. Which URLs were cited, and whether any are yours.
Compute the rates per assistant and per intent, branded and unbranded separately
Only then, if you must, blend.
Repeat within the week
At least a second pass on every question. Report the range, not the mean.
| Field | Why it is there |
|---|---|
| Assistant, date, question, intent, branded yes/no | So the row can be filtered and the run can be repeated |
| Verbatim answer | The evidence; nothing else is |
| Named (yes/no), first-mention offset | Presence and position without a model's opinion in the loop |
| Brands named, in order | Share of voice and who is taking it |
| Cited URLs, in order | Source; whether you own the sentence |
| Sentence about your brand, quoted | So a human can overrule any sentiment label later |
If you want the lightweight version first, the free AI visibility check shows the AI search answers that already mention your domain (mostly Google AI Overview today, some ChatGPT) and any whole sentences written about you. It is a draw, not a measurement, for the reasons above, and it is the fastest way to see whether the problem is presence, position or source.
The retrieval pipeline
Why do pages get dropped before the assistant reads them?
Because retrieval is a pipeline with three cuts in it, and each cut happens before the model has an opinion about your page. All three were described from the stage at Anthropic's Code with Claude conference in May 2026, by the people who build the harnesses.
Deduplication. In a production web-search agent, near-identical articles returned across several searches were collapsed into one before the model read anything. The effect: 77% fewer input tokens, 65% lower cost, and accuracy up 9%. Which copy survives is not something the page author controls. If your page says what four other pages say, you are in the 77%.
Tool-result pruning. The recommendation to agent builders was to clear tool results, search results included, at the end of every turn, keeping only "the decisions they informed". Your page lives inside an agent for one turn. What persists is the sentence the model wrote about it. If that sentence names you, you persist.
The small model reads first. The advisor pattern has a cheap model execute and a large model advise only when the executor is stuck. The model that runs the most searches is the one with the least judgment. It is not going to infer what your hero paragraph meant on turn fourteen of a task.
The implications for a page are concrete. Say something no other page in the answer set says, or be
deduplicated. Put the claim and the number in the first two hundred characters, because that is what a pruned
context keeps. Publish a plain-text or markdown twin the agent can fetch without a browser, because a cookie banner
and a hero video are what the small model sees otherwise. Every page on this site has one; append .md to the URL.
The plan is the table
How do you improve AI visibility?
Improve the specific rate that is low, on the specific assistant where it is low, for the specific intent where it is low. "Improve AI visibility" as a whole is not a task.
| What the run shows | What it usually means | The fix | How you know it worked |
|---|---|---|---|
| Not named on unbranded recommendation questions | Nothing on your domain answers "what should I buy for X" in a citable way | A page per buying question that states the recommendation, the tradeoff and the price in the first paragraph, with a plain-text twin | Presence on recommendation intent rises on the next fixed-set run; cited URL is yours |
| Named, described wrong | The assistant's source is stale: an old review, a competitor's comparison page, your former positioning | Correct the fact where the assistant is getting it and publish the current fact on your own domain in one consistent form | The verbatim sentence changes; the cited URL moves to your domain |
| Named, cited from a competitor's site | Their comparison page is the best structured source about you | Publish the comparison yourself, honestly, with the numbers, so the better source is yours | Cited URL flips |
| High on Perplexity, low on Claude or ChatGPT | The assistants weight sources differently; the one you are missing from leans on sources you are absent from | Check which sources the low assistant cites for your competitors and get present there: forum threads, encyclopedia entries, review platforms | That assistant's presence rate moves independently of the others |
| High on attribute, low on recommendation | You are documented, not recommended | Content that takes a position: best for, not for, at what price | Recommendation-intent presence closes the gap with attribute |
| Product name spelled three ways across the site | The deduplication step merges two of them away, or the assistant treats them as different things | One canonical name, everywhere, including schema and alt text | The entity appears consistently in verbatim answers |
Entity consistency beats volume. Assistants and their retrievers treat your brand, products and people as entities. One name, spelled one way, described the same way on your site and on the third-party sites the assistants lean on, does more than another dozen blog posts.
Corroboration off your domain is what makes your own domain believable. The assistants that named the brand least in the run above were the ones whose cited sources were forums and review platforms where the brand had no footprint. Being present where the assistant already looks is faster than teaching it to look at you.
Schema is labelling, not magic. FAQPage, Product, Organization and Article markup tell a machine what a passage is. They do not make a thin passage citable. Google's own guidance for AI features in Search says eligibility is the same as for ordinary snippets and calls splitting content into tiny chunks a myth. Write the self-contained answer first; label it second.
Expect three to six months for movement on a locked question set. Faster movement happens on narrow questions with weak competing sources. Anyone promising faster on head terms is selling the dashboard, not the outcome.
Same tools, different scoreboard
How is AI visibility different from SEO?
| Dimension | SEO | AI visibility |
|---|---|---|
| Unit of success | Rank position for a query on one engine | Presence, position and source in a generated answer, per assistant |
| Feedback | Impressions and clicks in Search Console, daily | None from the platforms. You see it only by asking the fixed questions yourself |
| Stability | A rank is stable enough to trend day to day | An answer is a draw; trend only on a fixed set with repeat sampling |
| What the reader does | Clicks a result | Usually does not click. Reads the sentence, or is an agent that never shows a person anything |
| Who reads the page | A person | Increasingly a small model on a task, for one turn, after a deduplication step |
| Levers that matter most | Links, titles, crawlability, intent match | Citable answers on buying-intent questions, entity consistency, corroboration on the sources each assistant uses, a plain-text twin |
| Where the work compounds | Your own domain | Your own domain plus the third-party pages the assistants cite |
Pages built for AI citation tend to rank in Google as a side effect, because the same properties (a direct answer at the top, entities named, tables instead of prose) are what snippets reward. The reverse is not true. A page can rank on page one and never be cited, because the assistant found a better structured source for the same fact.
For the record, this page ranks at position 84.8 for "ai visibility" in Google as of 2026-09-12, with 3,873 impressions and one click in the last 28 days, and this site has zero ChatGPT citations in the mention index we track. We publish that because a company that measures visibility and hides its own number should not be trusted with yours. The plan for this page is the plan in the table above; the fixed set is the one we run monthly.
The market
What are AI visibility services?
Three kinds, and the name on the invoice tells you less than which kind it is.
| Kind | What you get | What you bring | Watch for |
|---|---|---|---|
| Measurement dashboard | A prompt set run on a schedule, presence and share-of-voice charts, a blended score | The team to act on it | Whether the prompt set is yours or theirs, what share of it is branded, whether you can read the verbatim answers |
| Action platform | Measurement plus content production or optimisation, usually templated | Sign-off | Whether the content takes positions or restates what is already ranking; whether the trend line held the question set fixed |
| Hybrid audit and engagement | A fixed question set built with you, per-assistant and per-intent reporting, the verbatim sentences and cited sources, a plan tied to specific rates | Time on the question set | Whether movement is reported as a range from repeat sampling, and whether they tell you when a comparison is not valid |
Lynceus is the third kind. The question set is built for your category and split branded from unbranded, the run covers ChatGPT, Claude, Gemini and Perplexity, results are reported per assistant and per intent and never only as a blend, every answer is delivered verbatim with its cited sources, and a repeat-sampling stability figure comes with every rate. It is described on the BrandPulse page; the comparison pages cover how it differs from a search agency and from measurement-only tools.
Four questions to ask any vendor, in this order: Can I see the full prompt set. What share of it names my brand. Can I read the raw answers behind any number. Was the question set the same last month.
Not for you
When is measuring AI visibility not worth it?
Say no to a measurement engagement, ours included, in these cases.
Your buyers are not asking assistants.
Some categories still convert through a rep, a tender, or a store shelf. Ask ten customers how they shortlisted. If none used an assistant, spend the money on the channel they did use.
You cannot hold a question set fixed for six months.
If the category is being renamed, the product line is changing, or the person who owns the question set is leaving, the trend line will measure the churn, not the visibility. Wait.
You have fewer than a dozen citable pages.
A homepage, a pricing page and a contact form is a content problem, not a measurement problem. Measuring it produces a low number and no plan. Build the pages that answer buying questions first.
You are a local service business.
For "who is open Sunday" and "who can come today", the assistant reads structured business-profile data, not your site. Complete hours, services and Q&A on the profile matter more than any visibility report.
You want the number to go up in a month.
It will not, on a fixed set, on head questions. Anyone who tells you otherwise has changed the set.
And one line no vendor page carries: a high blended score is sometimes bad news. It usually means a branded-heavy prompt set and a brand whose customers already know it. The unbranded number is the one that predicts whether a stranger will ever hear of you.
Frequently asked questions
What is AI visibility?
AI visibility is whether, how often, and in what words an AI assistant names your brand when asked a question in your category. It is measured as presence, position and source, per assistant and per question intent, on a fixed set of questions. A single blended score is not AI visibility; it is an average of four different assistants and two kinds of questions, and it hides both.
How can I check my AI visibility?
Write twenty to forty category questions, most of them not naming your brand, and ask each in ChatGPT, Claude, Gemini and Perplexity in fresh sessions. Record the verbatim answers. Score whether you were named, where, and which URLs were cited. Compute the rates per assistant and per intent, branded and unbranded separately, and repeat the pass at least once. The free check on this site runs a three-question version in a few minutes.
What is a good AI visibility score?
There is no good single score. The rate that matters is presence on unbranded, buying-intent questions, per assistant. Lynceus reads under 20% as absent, 20 to 40% as occasional, 40 to 60% as contested, and over 60% as default, and treats any blended score as uninterpretable until the branded share of the question set is known. In a September 2026 run, branded questions added 17 points to the blend.
What are AI visibility services?
Measurement dashboards run a prompt set on a schedule and chart presence and share of voice. Action platforms add content production. Hybrid engagements build the question set with you, report per assistant and per intent with the verbatim answers and cited sources, and tie a plan to the specific rates that are low. Ask any provider for the prompt set, its branded share, the raw answers, and whether last month's set was the same.
How does AI visibility affect the buyer?
The assistant's answer narrows the buyer's consideration set before a search results page loads, usually without a click. A brand absent from a recommendation answer has lost that buyer silently; nothing in analytics records the answer that did not include you. Increasingly the reader is an agent on a task rather than a person, so the same narrowing happens many times across one job.
How do you improve AI visibility?
Find the specific rate that is low: which assistant, which intent, which failure mode (not named, named but wrong, named from a competitor's page). Then fix that: a citable page for the buying question, a corrected fact on the third-party source the assistant uses, a consistent entity name, a plain-text twin of every page, presence on the forums and review platforms the low assistant cites. Expect three to six months on a fixed question set.
What does AI visibility mean for a brand in SaaS or B2B?
The same three parts, with a different question set. B2B buyers ask assistants for comparisons, alternatives and "best for a team of N" questions, so the intent split leans toward comparison and use case rather than attribute. The 30-brand index on this site counts the AI search answers that mention each SaaS brand, so far almost all from Google AI Overview. The most-mentioned brand appears in over ten thousand of them; most brands in the index still sit at the 50-answer cap of the last run, so their true counts are not yet known.
How is AI visibility different from SEO?
SEO's unit is a rank for a query on one engine, with daily feedback from Search Console. AI visibility's unit is presence, position and source in a generated answer, per assistant, with no feedback from the platforms at all. The levers overlap (direct answers, entities, structure) but the success metric, the stability, and the reader are different. A page can rank and never be cited, or the reverse.
Related pages
Next step
Read your own paragraph
before another definition.
The free check shows the AI search answers that already mention your brand, mostly from Google AI Overview today. You will see whether the problem is presence, position or source in the verbatim text, and the table above tells you what that means.