7 Reasons AI Describes Your Product Wrong
Seven causes, and the useful thing about the list is that they split cleanly into two piles. Four you can act on today — stale third-party pages, your own site contradicting itself, missing facts nobody published, and a category you never stated plainly. Three you cannot reach at all — the knowledge cutoff, whether the model chose to search, and whatever it absorbed during training. Knowing which pile a specific error sits in is the difference between a morning's work and a year of frustration.
Sorted below by how much control you have, most first.
Key Takeaways
- Four of the seven are yours to fix, and the most common one is your own site saying different things in different places.
- A knowledge cutoff is a hard wall. The Gemini 3 Pro model card gives January 2025; anything later comes from retrieval or does not come at all.
- Retrieval is conditional — the model decides whether to search, so "it has web access" does not mean this answer used it.
- There is no correction channel for what a model absorbed in training. That is the pile to stop fighting.
- Publishing a dated, canonical facts page is the single action that pays back most, because it gives retrieval something unambiguous to find.
Reason 1 — Your own pages contradict each other
The most common cause and the easiest to fix. Pricing on the pricing page, different pricing in a two-year-old blog post, a third number in a help article, and a positioning line on the homepage that does not match the one in your docs.
Retrieval does not adjudicate. It reads what it finds, and when your own site offers three answers it will surface whichever page it landed on — often the oldest, because old pages accumulate links.
Fix: audit every page that states a price, a plan name, a limit or a category. Update or remove the stale ones. This is unglamorous and it resolves more wrong answers than anything else on the list.
Reason 2 — A stale third-party page is the retrievable source
A roundup from 2023 that lists your old pricing, a directory entry with your previous positioning, a review of a version you have replaced. These pages are frequently better-linked than your own current page, which makes them the easier thing to retrieve.
Fix: ask the publisher for a correction — most will, and it is a two-line email. Where they will not, publish a clearly better, current, dated page of your own and get it linked, so the correct version is the more retrievable one. This is slow and it works.
Reason 3 — The fact was never published anywhere clearly
Founding year, headquarters, what the product does not do, which category it belongs to, current plan names. If a fact appears nowhere in plain text on a page a crawler can read, a model has nothing to retrieve and will produce the most likely-looking version instead.
Fix: publish a canonical facts page on your own domain — plain text, dated, unambiguous, no marketing compression. Every fact a buyer or a model might need, stated once, in one place. It is the single artefact that does most for this whole problem.
Reason 4 — Your category is implied rather than stated
"The platform for modern teams" tells a model nothing. When your own copy never states the category in ordinary words, the model infers one from context — usually from whichever competitor set your page most resembles.
That is how a company gets described as something adjacent to what it is. Nobody wrote the wrong category; nobody wrote the right one either.
Fix: say it plainly somewhere prominent. "X is a [category] for [audience] that does [function]." Marketing language can live everywhere else.
The three below are outside your control. Recognising them saves the effort of trying.
Reason 5 — The knowledge cutoff
Every model has a date after which it knows nothing except what it retrieves. The Gemini 3 Pro model card states a cutoff of January 2025 (Gemini 3 Pro model card, retrieved 2026-09-06, SOURCED).
If you launched, repriced or repositioned after a model's cutoff, that model does not know unless it looks. This is not a fixable condition; it is a property of the system.
What you can do: make the current facts extremely easy to retrieve, so that when the model does look, it finds them fast.
Reason 6 — It did not search
Retrieval is conditional. Google's API documentation describes the model analysing the prompt and determining "if a Google Search can improve the answer", and bills "for each search query that the model decides to execute" (Gemini API grounding documentation, retrieved 2026-09-06, SOURCED).
So an answer in a search-capable session may still be pure recall. Users cannot tell, and neither can you unless the answer cites something.
What you can do: nothing directly. When you are testing what assistants say about you, record whether the answer cited anything — that single field tells you which pile the error belongs in. The method is in how to find out what ChatGPT says about your company.
Reason 7 — It is in the training data and there is no correction channel
If the wrong description came from training rather than retrieval, there is no form to fill in. No major provider documents a process for correcting a factual claim a model makes about a third-party company. Anthropic's support documentation offers a feedback control and a general support address, describes no formal process, and advises users not to rely on the assistant "as a singular source of truth" (Anthropic support documentation, retrieved 2026-09-06, SOURCED).
What you can do: make the correct version the dominant, most retrievable version of the fact, and wait. That is genuinely the whole available strategy, and pretending otherwise wastes months.
One thing that does not help as much as promised
Structured data is worth adding and it is not a control. Google states directly that it "does not guarantee that features that consume structured data will show up in search results" (Organization schema reference, retrieved 2026-09-06, SOURCED).
Google is blunter still about its AI features: "You don't need to create new machine readable files, AI text files, or markup to appear in these features. There's also no special schema.org structured data that you need to add" (AI features and your website, retrieved 2026-09-06, SOURCED).
Add Organization markup because it is cheap and correct. Do not buy anything sold as an AI-visibility markup product on the strength of it.
The order to work in
- Fix your own contradictions. Cheapest, highest yield.
- Publish the canonical facts page. Dated, plain, complete.
- State your category in ordinary words somewhere a crawler will read it.
- Chase the worst third-party page that keeps getting cited.
- Test monthly and record whether answers cite anything, so you stop spending effort on the unreachable pile.
The full picture of what is and is not documented is in AI misinformation about your brand.
How This Guide Was Sourced
Written and maintained by the LogicBalls editorial team (logicballs.com). Disclosure: LogicBalls builds AI writing tools. We would benefit from telling you this needs a product. It does not — four of the seven fixes are editing your own pages.
Sources. Knowledge cutoff: the Gemini 3 Pro model card. Conditional retrieval: Google's Gemini API grounding documentation. Structured data limits: Google Search Central's Organization reference and its AI features guide. Absent correction process: Anthropic's own support documentation. All retrieved 2026-09-06 and linked inline.
What could not be fetched. OpenAI's help documentation returned HTTP 403 to automated requests. No claim here is sourced to OpenAI material, and knowledge-cutoff behaviour is illustrated with the one model card we could read.
What is not claimed. We publish no measurement of how often AI describes products incorrectly, and no ranking of these seven causes by frequency. The ordering above is by how much control you have, which is a judgement, not a measurement.
No LogicBalls telemetry is used in this guide.
Frequently Asked Questions
Why does AI keep quoting our old pricing?
Usually because an old page still states it — yours or a third party's — and that page is more retrievable than your current one. Check your own site first; it is the more common cause.
Will adding schema fix it?
Not on its own. Google says structured data guarantees nothing about what appears in results, and that no special markup is needed for its AI features. Add it because it is correct, not as a remedy.
How long does a correction take to show up?
There is no published timeline from any provider. Retrieved facts can update as soon as the page is recrawled; trained facts have no correction route at all.
Should we publish a facts page even though nobody will read it?
Yes. Its audience is retrieval systems and journalists, not buyers. Date it, keep it plain, and update it when anything changes.
Is it worth contacting the AI provider?
For personal data there are established rights and processes. For a company fact, no provider we could reach documents one — so spend that effort on the retrievable sources instead.
