The AI Isn’t Wrong About Your Company. Your Sources Are.

Most companies now check what ChatGPT and Gemini say about them. Far fewer ask where the answer came from. The uncomfortable finding, for those who trace it, is that it usually came from them.

Ask an AI assistant to describe a company and it will answer with confidence: what the company makes, where it is based, who it competes with. For a growing number of brands, that answer is wrong in ways that cost real deals. A manufacturer is placed in the wrong country. A product that was discontinued three years ago is presented as the flagship. A company that merged, rebranded or moved upmarket is described as the company it used to be.

The standard response is to treat it as a content problem: publish more, add structured data, chase citations. Practitioners who have traced AI answers back to their origins say the error sits upstream of any single page.

“An AI engine is not inventing a story about your company,” says Aia Laser, founder of AlignTo AI, a Tel Aviv startup that helps brands take control of how and why AI systems represent them. “It is assembling one from three inputs: your own site, what third parties say about you, and what it has already learned from both. When those three disagree, the model averages them. Most brands have never checked whether they agree.”

Tracing the answer to its source

Laser’s claim rests on an audit of an industrial manufacturer, one of AlignTo AI’s first clients. Her team captured hundreds of AI answers about the brand across several engines and traced every claim in them to one of three places: the company’s own site, a third-party page, or no page at all.

The company’s site stated its headquarters and manufacturing locations correctly, but in a handful of places and not always where a machine would read them. Distributor listings and directory pages, written years earlier, described it as a US or European manufacturer. And in ChatGPT’s answers, the brand was named correctly in 88 percent of cases without any cited page carrying the information: the model had already learned the company from the whole corpus, and the corpus did not agree with itself.

“Your site is the one input you fully control, and it is upstream of everything else,” Laser says. “Third parties copy it, and the models learn from both. If your site says the right thing once, quietly, and twenty distributor pages say something else, the model will not side with you. It will side with the majority.”

“People hear that and conclude the site does not matter,” Laser says. “It is the opposite. The site is where the correct version has to live, stated clearly and in a form AI can read, because everything else is copied from it or compared against it. The mistake is thinking the site is where the work ends.”

The practical consequence is a familiar story in marketing departments this year. A team spends a quarter rewriting product pages, the dashboard shows citations climbing, and the AI assistant still tells prospects the company is headquartered in the wrong country, because the pages the engines trust most were never the company’s own.

The identity the machines already hold

Laser says the problem begins one step earlier than most people look. “Before a model decides what your company does, it has to decide which company you are. That step is called entity disambiguation, and nobody in marketing is watching it. If your name is common, or shared with a competitor, or also an ordinary word, the model may merge you with someone else’s facts, or split you into two half-entities that each carry part of your story. Everything it says afterward is built on that decision.”

The decision is not hidden, she argues, and that is the part the industry misses. “Google’s Knowledge Graph is a database with an API. Wikidata is open. You can query them for your own entity and see exactly what the machines hold: what name and type they have for you, the one-line description they attach, which aliases they recognize, what identifier they assigned, and how confident the match is. That is real knowledge-graph probing, and it takes minutes. I have never had a company do it before we did it for them.”

What comes back is frequently a shock. “We have seen companies that exist in the graph as the wrong type, or under a name they stopped using years ago, or not at all. When the record is missing or wrong, every engine that leans on it is guessing, and it will guess from whatever third-party page it finds. So when an AI answer has no cited page behind it, the first question is not what the site says. It is what the identity record says.”

Who the memory hurts

A startup founded last year has little to fear from this. There is not much for a model to remember. The companies most exposed are the ones with the longest histories and the most to lose from being described as their former selves.

Post-merger companies are the clearest case. The acquired brand still exists in years of press coverage, directory listings and old partner pages, and the model has absorbed all of it. Long-established firms carry a legacy perception that may be a decade out of date. International companies find that each market has told a slightly different story about them, and the model has averaged the stories together. Any company that repositioned, dropped a product line or moved into a new category is, in the model’s memory, still where it started.

“The companies that come to us are not companies with bad content,” Laser says. “They are companies with a past. The model is faithfully describing a version of them that is still live in a hundred places they do not control, and usually in a few places they do. Nobody ever went back and reconciled the story.”

The damage is quiet. Nobody sees the deal that was never opened because a buyer asked an assistant for a shortlist and the company appeared under the wrong description, or did not appear at all.

Three causes, one diagnosis

The deeper problem, Laser argues, is that an AI misrepresentation can have three different causes, and each demands a different response. The industry mostly treats them as one.

“Attribution is the whole game,” she says. “If you cannot say which bucket a mistake sits in, you will spend the content budget on an error that content cannot touch, and ignore a distributor page that is quietly teaching every engine the wrong headquarters.”

Monitoring tools, in her view, are good at the first half of the job. They show what the engines are saying. What they rarely do is explain why, because explaining why requires a confirmed record of what is actually true about the company, and most companies have never written one down.

Showing a brand what AI knows about it

AlignTo AI was built to answer the question most monitoring tools leave open: not only what the engines say about a brand, but where each claim came from and why. The startup shows companies what AI currently knows about them, traces every misrepresentation back to its root source, whether on the brand’s own site, on a third-party page, or in the model’s identity record, and gives the company a way to decide and control how it is represented from there.

Aia Laser, who also founded the Tel Aviv B2B tech digital marketing agency (Inter-Dev),  spent over a decade in leading SEO strategies for renowned Israel’s tech companies, before AI answers became a channel, frames it in plainer terms. “Would you let a new salesperson go out and talk about your brand without handing them the updated, correct data first? That is what every company is doing with AI right now. The engines are the salespeople. They are out there describing you to buyers all day, and nobody at the company has checked what they were trained on or told them what changed.”

The argument is that representation in AI is a governance problem, not a content problem.

“Brands have been outsourcing their own definition to whoever wrote about them last,” she adds. “AI just made the cost of that visible.”

What to do before the next audit

Aia Laser’s advice to companies that suspect the model has them wrong is to stop optimizing and start diagnosing.

1. Capture answers with their citations, then trace every claim: is it on your site, on a third-party page, or on neither? That tells you where the model learned it.
2. Write down what is true. One confirmed record of the company’s facts, agreed internally, is the baseline every audit needs and almost no company has.
3. Check your own site against that record first. Every fact should be stated, consistent across pages, and readable by a machine.
4. Sort every remaining error by cause before spending on any of them, and treat a change that shipped and moved nothing as information about where the error really lives.

“Everyone is asking how to get the AI to notice them,” she says.

“The harder question is what the AI already thinks it knows, and whether anyone at the company has ever decided what the answer should be.”