A founder showed me a ChatGPT answer about his own company last month. It stated, plainly and with no hedging, that his product did not have an API. It does. It has had one for two years. The engine was not framing him unfavorably or leaving him off a list. It was just wrong, stating a false fact about his business as if it were settled, to every buyer who asked.
This is a different problem from being described unkindly, and it needs a different fix. Being called “the lightweight option” is a matter of framing, and you change framing by changing the sources, which I covered in how to fix the way AI search describes your brand. Being told you have no API is a matter of fact, and a wrong fact behaves differently. It is more damaging, because a buyer who hears “no API” simply crosses you off and never asks again. And it is more fixable than it feels, as long as you understand why the engine is wrong in the first place.
Why the engine is confidently wrong
AI engines are not lying to you. They are repeating something, and the something is usually one of three things.
It is repeating an old version of you. The model may be answering from memory, from a training snapshot taken before you shipped the API, raised the price, or dropped the feature. It is not wrong about the present so much as stuck in the past, and it does not know the difference. This is the same memory-versus-live split that governs how long it takes to get cited at all, working against you instead of for you.
It is repeating a bad source. If the clearest thing written about your API is a three-year-old forum post that said you did not have one yet, and nothing more authoritative has corrected it, that post is what the engine learned from. It is repeating someone else’s stale homework.
Or it is filling a gap. If almost nothing clear exists about a specific fact, the engine sometimes guesses based on what is typical for companies like yours, and presents the guess with the same confidence as a fact it actually knows. Silence does not read as “unknown” to an engine. It reads as permission to assume.
In all three cases the lesson is the same. The wrong fact does not live on your website. So you cannot fix it on your website by simply stating the truth louder.
Why correcting your own homepage does almost nothing
The instinct, every time, is to go put the correct fact prominently on your own site and assume that settles it. It rarely does, for two reasons.
First, a memory-based answer will not see your update until the model refreshes, which happens on its own schedule, not yours. You can correct your homepage today and the wrong answer can persist for months on the engines that answer from training.
Second, and this is the part people resist, your own assertion is the weakest evidence on the web that something about you is true. Every company claims to be great, so engines discount self-description heavily. A team I worked with had quietly added “full REST API” to their homepage three times in different words, and the wrong answer did not move. The engine was not short on claims from them. It was short on a credible source it actually trusts confirming the fact.
How to actually correct a wrong fact
The fix is a source and freshness operation, run in roughly this order.
Find what the engine is citing. Run the exact question that produces the wrong answer, on the engines that show sources, and read what they pull. Often the false fact traces to one or two specific stale pages, a dated review, an old comparison table, a forum thread. That is good news, because a small number of sources is a fixable number.
Correct it at those sources where you can. If a third-party review still lists a feature you have had for two years as missing, that page is doing more damage than your homepage can undo, and getting it updated is the single highest-leverage move available. This is the same source-layer logic as fixing a label, pointed at a fact.
Make the correct fact unmissable and current on your own pages. Not as a marketing claim, but as a plain, dated, specific statement an engine can lift verbatim. “Yes, [product] has a REST API. It launched in 2024 and supports X and Y.” Specific and verifiable beats confident and vague, every time, which is the heart of how AI engines decide what to cite.
Earn one or two outside confirmations. A single credible third-party page or doc that states the correct fact does more than ten assertions from you, because it is the kind of source the engine was missing in the first place.
Then re-check on a schedule. The retrieval engines will reflect the correction within weeks of recrawling. The memory engines will lag until they refresh, and there is no button to force it, so you confirm the fix landed by re-running the query over time rather than assuming the day you publish is the day it changes. Tracking that on purpose is what measuring your AI search visibility is for.
What I got wrong
The first time I helped a company fight a wrong fact, I treated it as an emergency and threw everything at their own site in a week, on the theory that more correct statements from them would overwhelm the error. Nothing moved for a month, and I assumed the work had failed.
It had not failed. It had been aimed at the wrong layer. The wrong fact was anchored in two outdated third-party pages and a memory-based model that had not refreshed. Once we got one of those pages corrected and waited out a refresh cycle, the answer flipped. The lesson stuck: a wrong fact is not an emergency you can shout down on your own turf. It is a source problem and a timing problem, and both have to be worked patiently.
Why this matters
A bad framing costs you some prominence. A wrong fact costs you the buyer outright, silently, before they ever reach you, and it keeps doing it on every query until it is corrected at the root. It is also one of the clearest signals a prospect has that nobody is minding their AI presence, because the error is so concrete and so checkable.
The reassuring part is that wrong facts are among the most fixable problems in AI search, far more than soft framing, because a fact has a specific source and a specific correct answer. You just have to fix it where it actually lives.
If you want to know what AI is currently telling buyers about your company, including the things that are simply not true, that is the first thing a paid audit surfaces, and the full method is on the methodology page.