Background
The errors are not random. Four mechanisms produce almost all of them, and not one of them shows in the wording of the answer.
01
A model without search access is not reading your site. It repeats training data that stopped on a fixed date. A rebrand or a sale after that date does not exist for it, and nothing in the wording tells you the answer is old.
Training cutoff
02
Models do not weigh everything they read the same way. A public entity record states a name and a type in a form that survives training, and ordinary web copy does not. Where that record exists, the description tends to be right. Without one, the model has nothing to hold on to.
Structured sources
03
The worst of the four, and the hardest to spot from the inside. Another organisation holds the public record for your name, so the model describes them when asked about you. It matched a name to a record, and the reader has no way to tell.
Name collision
04
Asked about a company it does not recall, a model still produces a plausible profile, built from what the name suggests and from businesses that look similar. It reads exactly like a correct answer. A blank would be honest. Invented detail is what a reader repeats.
Confident filling
One answer, taken apart
A model was asked about Kelvane Filtration, which builds water treatment plant for utilities in the north of England. It had no web access and was never shown the website. This is what came back. Eight clauses, four mechanisms, and nothing in the writing that separates one from another.
The answer, as it was given
Kelvane Filtration is a private limited company, incorporated in 2004 and registered in England. It supplies filter media and replacement cartridges to food and drink processors, working mainly with customers in East Anglia. The business remains independently owned, and production runs from a single site in Sheffield. Its accounts are filed as those of a small company, and the registered address has not changed since incorporation. It employs somewhere in the region of 120 people and is generally regarded as one of the steadier names in its part of the market.
Model B, asked by name, cold. Nothing shown to it.
Eight clauses, in the order they were written
Pick a mechanism
Read it once before picking anything. It is detailed and it never hedges. Nothing in it marks which clauses have a source. Pick a mechanism to see the clauses it produced.
01Training cutoff2 of 8 clauses
It repeated two facts that were true when its training data was collected. Kelvane was independent then, and Sheffield was the only plant. The sale went through afterwards, and a second site opened the year after. Both clauses were correct when the model learned them. It has no way of knowing how long ago that was.
Old facts and current facts are written in the same tense, with the same confidence. Nothing marks a clause learned four years ago as older than the one beside it.
The cutoff cannot be moved. What gets collected next can. State the change plainly somewhere that stays put, and use the same wording everywhere the company is described.
02Structured sources2 of 8 clauses
It leaned on one public record carrying the name and used it for what a record states well: legal form, incorporation date, registered address. Those are the most precise clauses in the paragraph and the only two the register states outright. The method is sound. Everything rests on the record being the right one.
A sourced clause and a recalled clause are written identically. The answer cites nothing, so the two facts that could be checked look no different from the six that cannot.
Check that the record filed under the name is yours and describes what you do. Then publish the same identity on your own domain in a form a machine can read, so the two sources agree.
03Name collision2 of 8 clauses
The record it leaned on belongs to a different company trading under the same name. The sector and the customers in this answer are that company's. Nothing was mixed up. The model did not weigh two records and pick the wrong one. There was only ever one, and it was not this business.
The subject changes without the sentence changing shape. Same name, same tense, and a description that holds together on its own terms.
Give a machine something to tell the two apart. Full legal name, registration number and registered address, published together on your own site and matching the register.
04Confident filling2 of 8 clauses
Two clauses have no source. The headcount is a round figure that fits a company described this way, and the closing judgement is a sentence pattern rather than a finding. Neither was recalled. Both were assembled because a company profile is expected to end with a number and a verdict.
Invented clauses read a little better than sourced ones, since nothing constrains the wording. Hedging would be the signal to look for, and there is none in the paragraph.
Fill the gap first. Where a number matters, publish it somewhere stable so there is something to recall. Gaps get filled either way, and you can decide with what.
The model did nothing wrong by its own lights. It was asked about a name, matched it to the best record it had, and wrote the rest the way a company profile is usually written. Every step of that is reasonable. The answer is still about somebody else.
Kelvane Filtration, the answer above and every name, number and value in it are invented for this example.
Arguing with a chatbot changes nothing, and neither does rewriting your homepage. The answer moves when the sources behind it change: a correct entity record under your name, and structured data on your own domain stating what the organisation is.
That is a publishing problem, not a technical one. It is slow, and it is the only thing that works. The scan asks four models about you, runs six checks and shows the evidence behind each finding, so you know what needs correcting before anyone spends money on it.
Check your company