AI brand perception: how ChatGPT, Claude, Gemini, Google and Meta AI describe your brand

Visibility measures whether AI names you. Perception measures what it says when it does. They are measured separately because they fail separately, and together they produce four very different situations:

• Visible and well described: AI names you and describes you the way you want. The job is to keep it that way and check that it doesn’t drift.
• Visible and poorly described: AI names you, but with an outdated fact, an exaggerated flaw, or without what sets you apart. This is the worst of the four, because AI amplifies a story that works against you.
• Invisible, but well described when asked by name: AI knows you but doesn’t bring you up when the question is about the category. That is a visibility problem, worked on through share of voice and content.
• Invisible and undescribed: AI has no material about you. First you have to exist.

For visibility, read the guide to AI share of voice. This guide covers perception: what gets measured, how to read it, and what to do about it.

Sentiment analysis scores the tone in which each answer talks about your brand on a 0–100 scale. In Refractia the bands are: 70 or higher, positive; 40 to 69, mixed; below 40, negative. A high average is good news, but it doesn’t tell you why, and two brands with the same score can need opposite actions:

Seguros Lince has to address one specific pain point, with facts AI can repeat. Aseguradora Brisa needs a story of its own. The score is identical; the work isn’t. That is why sentiment is always read next to the descriptors, and per engine: a chat engine can carry a criticism from years ago that AI search no longer shows.

Descriptors are the adjectives and short phrases engines use to characterize you: “reliable,” “expensive,” “pioneering,” “hard to reach.” Aggregated across hundreds of answers, they show the brand image AI has put together, in more detail than any score.

• Frequency: which words repeat most. The ones that appear in most answers are the core image; the ones that appear once are noise.
• Tone: which descriptors are positive, negative or neutral. One heavily repeated negative weighs more than ten scattered positives.
• Vocabulary diversity: how many different terms AI uses to describe you. A few repeated terms point to a consistent image; many scattered terms point to a blurry one, where every answer says something different.
• Differences by engine: if ChatGPT calls you “traditional” and Gemini calls you “innovative,” there isn’t one AI perception but several, and you want to know which one your customer sees.

Every brand has traits it wants to be known for: fast, approachable, safe, premium. Trait alignment compares those desired traits with what AI actually says. The trap is counting only whether the trait appears: if “innovative” shows up twice among three hundred mentions, it is technically there, but nobody perceives it.

That is why alignment is weighted by prominence: a trait counts in proportion to how often it repeats compared with the rest of the vocabulary. A brand that wants to be “fast, approachable and innovative” and gets “fast” all the time, “approachable” now and then and “innovative” never is not aligned on three out of three, even though two of the three traits “appear.”

The comparison with competitors completes the picture. A perception matrix shows, trait by trait, whether AI assigns it to you more, equally or less than to each rival: wins, ties and losses. Sometimes the trait you want is exactly the one AI gives to someone else.

Beyond individual words, AI tells a story about your brand: where it comes from, who it is for, what it stands out for, and what it gets criticized for. That narrative sorts into three parts that help you decide:

• Strengths: what AI consistently says in your favor. This is what you need to keep backing with fresh evidence.
• Vulnerabilities: the negatives that repeat, whether a real problem, an outdated fact or an unfavorable comparison. This is the first thing to investigate.
• Opportunities: what AI doesn’t say and could. Desired traits that are missing, topics where your competitors show up and you don’t, uses nobody attributes to you.

A fictional example: a gym chain in Colombia whose AI narrative is “cheap and with lots of locations, but with old equipment and crowded at peak hours.” The strength is price and coverage; the vulnerability is the in-gym experience; the opportunity is that no answer mentions its group classes, which are its big bet for the year.

AI doesn’t hold a single perception of your brand: it answers differently depending on who is asking and what they ask. A buyer, an investor and a job candidate ask different questions, and engines look in different sources to answer them.

To measure it, build a set of questions for each audience and compare them on the same engines. Add “who is … for?” questions too: they show which segment AI associates you with. If you want to sell to small businesses and AI only recommends you for large companies, you have a positioning problem that no visibility metric will show.

Your brand’s perception in AI moves even if you do nothing: models get updated, the web publishes new articles, and your competitors change their content. You detect that drift by comparing scans, on one condition: the same questions, on the same engines, in the same country. If you change the questions, you can’t tell whether the perception changed or the way of asking did.

When you compare, look at the sample. A jump in sentiment across ten answers can be noise; the same jump across three hundred is a signal. And look at which descriptors came in and which dropped out: it is usually the first clue to what new source AI is reading.

Each perception finding calls for a different kind of action. Before you produce any content, identify which one you are dealing with:

• A false or outdated fact: find the source and correct it. The guide to protecting your reputation in AI explains how.
• A fair criticism: if AI says your service is slow and it is, the work is operational, not communications. Content doesn’t cover up a real problem.
• A desired trait that’s missing: publish evidence that proves it, with data, cases and third-party coverage, not adjectives about yourself. AI repeats facts more than promises.
• An unfavorable comparison: check which comparison pages exist in your category and your country, and whether your brand appears in them with current data.
• Differences between chat and AI search: if AI search already describes you well and chat doesn’t, the problem is the model’s memory. The web is already fixed; it takes time and sustained presence.

The AI Perception module shows, for each engine, sentiment from 0 to 100, the descriptors it uses, trait alignment weighted by prominence, vocabulary diversity, a positioning radar, and a perception matrix against your competitors with wins, ties and losses per trait. The narrative is laid out as Strengths, Vulnerabilities and Opportunities. The desired traits come from Identity & DNA, the brand profile Refractia builds by reading your website.

Evolution shows how sentiment changes for your brand and each competitor between scans, and the Prompts Library stores every question with each engine’s answer, so you can read the source behind any number. We measure ChatGPT, Claude, Gemini, Google’s AI Overviews and Meta AI, and every scan is started by a person.