AI gets one in eight brand facts wrong
We asked AI to describe 26 Indian consumer brands and verified every checkable claim against the brands' own sites. Roughly 18 percent were wrong or outdated, and three brands were described as an entirely different company.
We asked AI to describe 26 Indian consumer brands, pulled every checkable factual claim it made, and verified each one against the brand's own live website. Roughly 18 percent of those claims were wrong or out of date. Three of the 26 brands were not merely described inaccurately. They were described as an entirely different company.
The industry conversation about AI and brands is almost entirely about presence: are you mentioned, are you recommended, what is your share of voice. That conversation assumes the mention is correct. Our data says that assumption fails about one time in eight.
What we actually measured
The method was deliberately boring, because boring is what makes it checkable. For each brand we asked the model to describe the company, its pricing, its main products and its key features. We then extracted the specific, verifiable claims from that answer, the kind a buyer would act on, and compared each against the brand's own website as ground truth. Each claim was marked true, false, outdated, or unverifiable.
We excluded anything subjective. “Popular among young buyers” is not checkable. “Starts at 599 rupees” is. That leaves a smaller sample than a mention-counting study would produce, but every data point in it means something.
The finding nobody expects: it is describing a different company
The most striking failures were not hallucinated details. They were cases where the model had confidently retrieved the wrong entity entirely.
Asked about Plum, the Indian skincare brand, the model produced a fluent, detailed and completely accurate description of Plum, the UK fintech company. Snitch, the men's fashion label, was described as a home security product. Deconstruct, the skincare brand, came back as a plant-based meat company. Three of 26 brands, more than one in ten, were the victim of mistaken identity.
This matters because it is a different problem with a different fix. A stale price is a freshness issue: publish the current number and the retrieval layer catches up. Entity confusion is an identity issue. The model does not know you are a distinct company from the other one sharing your name, and no amount of updating your pricing page fixes that. It is solved with disambiguation work: structured data, a clear knowledge-graph presence, and consistent naming across the high-authority web.
Why the model sounds so sure
A language model predicts the next most probable piece of text. It has no internal flag that fires when it is guessing. Reinforcement learning from human feedback then trains it to sound helpful and decisive, because helpful and decisive is what human raters preferred. The result is a system that delivers a wrong price in exactly the same confident register as a correct one.
This is not a defect that gets patched in the next release. It is a property of the method. Retrieval helps, grounding an answer in a fetched page reduces invention, but it does not eliminate it: a grounded answer can still carry a stale fact forward from training memory, or ground itself on a third-party page that is itself out of date.
What this costs a brand
Consider the mechanics. A buyer asks an assistant which product to choose. The assistant names you, which by the standards of every AI visibility tool on the market is a win. It then quotes a price you abandoned eight months ago, or attributes a feature you do not have, or describes a different company's product line. The buyer either arrives with wrong expectations or does not arrive at all.
Your analytics show nothing, because there was no click. Your AI visibility dashboard, if you have one, shows a mention and a green number. The failure is invisible at exactly the layer most teams are measuring.
What to do about it
First, check. Ask the engines the questions your buyers ask and read what comes back, in full, not just whether your name appears. Do it repeatedly, because the output varies between runs.
Second, fix your own ground truth. Models retrieve from your live pages, so outdated pricing, specs and product pages on your own site are the cheapest errors to eliminate.
Third, fix the third-party record. Old listicles and stale directory entries carrying your former pricing are actively teaching the model wrong facts. These are worth chasing down.
Fourth, if you share a name with another company, treat disambiguation as infrastructure. Structured data, a clean Wikidata and knowledge-graph presence, and consistent naming everywhere are what teach the model you are a separate entity.
Common questions
How often does AI get brand facts wrong?
In our test of 26 Indian consumer brands, roughly 18 percent of the checkable factual claims AI made were wrong or outdated. A further three brands were described as an entirely different company that happened to share the name.
Why does AI state wrong facts so confidently?
Language models are trained to produce fluent, helpful-sounding text, not calibrated uncertainty. They have no internal signal that says 'I am unsure', so a guessed price arrives in the same confident tone as a verified one.
Can I correct what AI says about my brand?
Not directly. You cannot edit a model's weights. You can update your own site and the high-authority third-party pages models retrieve from, which fixes grounded answers quickly and training-memory answers over the next model generation.
How do I check what AI says about my brand?
Ask the engines the questions your buyers ask, many times each, and record the answers. Because output is probabilistic, a single check is unreliable. Tools like Aelo automate the sampling and verify each factual claim against your live site.
The full methodology, sample sizes and per-brand receipts are published in the India AI Visibility Index. You can also run a free scan on your own brand and read exactly what the AI says about you.
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