How ChatGPT, Perplexity, Gemini, and Claude describe the companies buyers ask them about. Measured across four signals, in their own words.
For 24 of the 503 companies in this benchmark, AI gets more than one in five facts wrong. For 7 of them, more than three in ten. That is not a rounding problem. It is a pattern.
Across 503 companies in 21 industries, scanned on four assistants, the average Brand Accuracy Rate was 90.1% (median 90.9%). Roughly one in ten verifiable claims AI makes about the typical company is wrong or outdated. Eighty-two companies scored a perfect 100%.
Accuracy is the headline, but it is not the whole picture. The benchmark measures four signals for every company: accuracy, AI visibility, machine-readability, and how well a brand's own narrative survives being retold. Read together, they explain not just whether AI is wrong, but why.
We measured accuracy directly. For every company we compared what the assistants say against what its own site and trusted sources state, then sorted each failure into one of three modes: identity divergence, erasure and fabrication, and the homonym trap. Every mode has a distinct cause and needs a different fix. Each one also reveals something about how these models build a picture of a business, working from its name, its structured data, and whatever third parties have written about it.
Read one way, a 90.1% average is reassuring. Read it the way a buyer does, though, and the math shifts. One assistant, one answer, one moment of decision. A single wrong claim about your category, your pricing, or your ownership becomes the whole impression. This report is about those single wrong claims. Where they cluster, why they happen, and what moves them.
Most companies score well. The problem lives in a concentrated tail. There is also a surprisingly large group sitting at a flawless 100%.
The 24 companies below 80% are where the story lives. They are not spread evenly across industries or platforms. Instead they cluster around three specific failure modes, and a company usually fails for exactly one of them. Identify the mode and the fix is obvious. Miss it, and you can pour months into the wrong remedy, adding schema when the real problem is that your name is also an English word.
Four pillars, one benchmark. Brand Accuracy Rate is the headline; the other three explain why a score lands where it does.
The share of verifiable AI claims that match the company's own site or trusted sources: pricing, capabilities, leadership, founding date. The headline metric.
A 60/40 blend of mention rate and prominence across buyer-intent queries. Platform breadth was dropped this year because it behaved as a near-constant.
Machine-readability of the company's own pages, built from schema coverage and content readability. Both are Six Signals in their own right, listed next.
How well a brand's own narrative holds up in AI answers, versus being reframed by competitors or misrepresented outright.
The six cells of the Faro mark are not decoration. Each one is a signal we measure, and together they compose every score in this report.
Accuracy tells you whether AI is right about you. These three tell you why, and where the leverage sits.
Mean machine-readability across the field, and the single biggest gap in the dataset. Most sites are only two-thirds legible to a model. Strong schema helps a model retrieve you correctly. It will not, on its own, fix a name that collides with a common word.
Necessary, not sufficientMean score for how well a brand's own narrative survives being retold. Generally strong. The tail is where it breaks. Competitors reframe you, or an assistant quietly swaps a rival's positioning in for yours, in an answer the buyer reads as neutral.
Strong center, exposed tailMean across the field, with a standard deviation of just 2.8. That band is too narrow to separate companies meaningfully this year. Visibility tells you whether AI mentions you. Accuracy tells you whether what it says is true. Only one of them loses a deal.
Too narrow to rank onWhen the platforms fail, they do not fail identically. Each has a characteristic way of getting a company wrong, and knowing the tendency tells you where to look first.
Most likely to say a company "does not exist" when retrieval comes up empty, or to open with the dictionary meaning of a brand name. When it is right, it is often the most precise of the four. When it misses, it misses absolutely.
Rarely admits a gap. Where it lacks grounding it constructs a plausible-sounding profile: a category, a use case, sometimes even pricing. It arrives with full fluency and no hedging, which makes it the most convincing wrong answer of the set.
Tends to build an identity from the letters of the name itself. It reads "assembly" as manufacturing, or invents a mapping product from a company whose name sounds geographic. The failure is lexical, not factual.
The most likely to say plainly that it does not have reliable information. Safer for accuracy. For a buyer-intent query, though, silence reads as "not a real vendor," which is its own kind of cost.
Every score in this report traces back to real answers. Here are the recurring types. The same handful of shapes appears again and again across 503 companies.
"A cloud-based legal case management platform for law firms."
A legal case-management platform. Exactly right, and the benchmark's best case. This is what the other three should have matched.
"…for insurance companies and third-party administrators (TPAs)…"
The same platform. Fluent, specific, and the wrong industry entirely. No hedging at all, which is what makes it the most dangerous kind of miss.
"…Geographic Information Systems (GIS) and Location Intelligence…"
A marketing-intelligence tool. No footprint to retrieve, so the model built a product out of what the name sounds like.
"…do not contain information about a tool named [company]."
A marketing-intelligence tool. Erased outright. For a buyer query, "does not exist" is the worst possible answer.
"I don't have reliable information about a product called [company]."
The same tool. Accurate about its own limits. Silence still reads as "not a real vendor."
Describes the firm as still independent, years after it was acquired.
An established PR firm. Right category, outdated fact. One platform caught the acquisition; the others did not.
Worst to best, by mean Brand Accuracy Rate across all 21 industries. Marketing intelligence sits far below the field; everything else is tightly packed.
Lowest is marketing intelligence at 54.4%. That is a small cohort of four, pulled down by the erasure cases in Failure Mode 2, so read it as directional rather than definitive. Fintech, long assumed the hardest vertical, averaged 91.6%. The counts across all 21 industries sum to 503.