AI visibility: what it is and how to measure it

What is AI visibility?

Someone asks ChatGPT which accounting software suits a small agency. The answer names four products, recommends one and links two sources. If your product is in that answer, you are visible for that question. If it is not, the buyer may never see a list of links where you could compete.

AI visibility adds that up across many questions and several engines. It tells you how often the engines name you, whether they recommend you, where you sit among the brands they name and whether they cite a source about you. It also tells you who gets named when you don't.

The questions that matter are unbranded. A buyer asks for the best project management tool for a remote startup, not for your brand by name. Your visibility depends on whether the engines bring you up on their own.

LLM visibility, AI search visibility and AI brand visibility

You will see several names for the same idea. They differ mostly in which surface they point at.

• LLM visibility — your presence in answers from chat assistants built on large language models, such as ChatGPT, Gemini, Claude and Perplexity.
• AI search visibility — your presence in AI features inside search engines, such as Google AI Overviews and Google AI Mode.
• AI brand visibility — the brand angle: whether you are named, how you are described and which competitors appear next to you.

The work of improving it has several names too: generative engine optimization, answer engine optimization and LLM SEO. They overlap more than they differ, and all three aim at the same result — being named in the answer. If the labels blur together, AEO vs GEO sorts them out.

How AI visibility differs from search rankings

In search, a page holds a position on a results page, and a rank tracker can read it. In an AI answer, there is no fixed list of ten. The engine writes a new answer each time, names a handful of brands and may or may not cite its sources.

That changes what you measure. Ask the same question twice and you can get two different answers. Ask ChatGPT, Gemini and Perplexity the same question and they rarely agree about a brand. A single test from your own browser tells you little. A useful measurement asks each question more than once, on each engine, and reports shares.

Search still matters, because engines that browse the web pick their sources from pages they can find. How the two disciplines fit together is covered in AEO vs SEO.

Mentioned, recommended, cited: three levels of presence

Being named is the lowest bar. An answer that lists five brands and then says which one it would pick has mentioned five and recommended one. For a buyer, only the recommendation reads like advice.

• Mention — the brand appears somewhere in the answer.
• Recommendation — the AI explicitly recommends the brand or one of its products.
• Prominent recommendation — the brand appears among the main recommendations or near the beginning of the answer.

Citations are a separate signal. An engine that shows its sources may link to a page about you — your own site, a review or a retailer — whether or not it recommends you. A citation tells you which pages the engine leans on for the topic.

Keep these apart when you read any number. Visibility alone does not necessarily translate into commercial value. A high mention rate with a low recommendation rate means the engines know you and still pick someone else.

How to measure AI visibility

Measuring AI visibility is closer to running a survey than to checking a rank. You choose the questions, ask them the same way each time and count what comes back.

• Pick the questions your customers ask: product discovery, comparison, purchase intent and informational questions. Leave your brand name out.
• Run each question on each engine more than once, and count a brand as present only when the majority of runs agree.
• Score each engine separately before you combine them. An average hides the engine where you are missing.
• Record every brand named in the same answers, so you see who gets recommended instead.
• Note which sources the engines cite, where they show them.

You can do this by hand for a handful of questions. For 20 questions on three engines, a free AI visibility checker is faster: Llamat runs up to 20 questions on ChatGPT, Gemini and Perplexity and sends one report by e-mail. If you need the same numbers every day, compare AI visibility tools built for ongoing tracking, or an AI Overviews tracker if Google is the surface you care about.

AI visibility optimization: what moves the numbers

Engines name brands they can find, understand and trust on a topic. There is no switch to flip. The levers are well known, and most of them are ordinary marketing work done with a specific question in mind.

• Pages that answer the questions buyers ask, in plain words, with the answer near the top.
• Product information that says what the product is, who it is for and how it compares — not only what it costs.
• Third-party coverage on the retailers, review websites, editorial media and forums the engines draw on.
• Consistent facts about you everywhere, so the engines don't have to choose between versions.

Google's AI features follow Google's own rules of eligibility. The practical steps, based on Google's documentation, are in how to rank in AI Overviews.

Reading the numbers without over-reading them

An AI visibility score is a snapshot. Answers change as models update and as the web changes, so results reflect what the engines said during the period you measured.

Tools define their metrics differently. One counts a check per prompt per engine, another counts credits, another counts the prompts you add rather than the checks it runs. Compare scores from the same tool and the same set of questions, and compare yourself with the competitors in those answers — not with a benchmark built on someone else's questions.

The most useful comparison is with yourself. Measure, change one thing, then ask the same questions again. That second reading is what turns a number into a finding.