Why AI Describes Your Company Vaguely - and What Helps

You ask ChatGPT what your company does and get an answer you cannot dispute. None of it is invented. It is still wrong: the service that generates 70 percent of your revenue does not appear. Instead there is something you offered years ago. The description would fit every second company in your industry.

This is not an edge case, and usually not a fault of the model. It is the answer that emerges from ambiguous input.

Key takeaway: Blurred AI descriptions have two documented causes, and neither is invention. First, language models assign a name mention to the correct entity in only about three out of four cases; structured category information raises that figure measurably. Second, the most common error in AI answers is not the false claim but the omission: in a study of 98,020 individual claims from Google AI Overviews, 11 percent were unsupported by the cited source, mostly because context fell away. Being found unambiguously therefore requires not a longer self-description, but sentences that still hold up when shortened.

Why does AI describe my company vaguely rather than incorrectly?

Because two distinct processes create vagueness long before anything gets invented. The first concerns attribution: the system has to decide which real company a name refers to. The second concerns compression: many sources become a short text, and whatever does not fit into three sentences drops out.

Both processes have been measured, and the figures are uncomfortable. They also explain why the obvious response, simply writing more about yourself, often makes matters worse.

How often does a model assign a mention to the wrong company?

More often than expected. Work by Gerard Pons, Besim Bilalli and Anna Queralt at the Universitat Politècnica de Catalunya measured how reliably language models link a name mention in text to the correct entity in a knowledge graph. The result across ten datasets: a language model without assistance reaches 75.4 percent. Every fourth attribution therefore goes to the wrong entity.

What raises the figure is instructive. The researchers supplied the model with the knowledge graph's class hierarchy, that is, information about which category a candidate belongs to. That lifted accuracy to 81.1 percent, close to a specialised model trained specifically for the task, which reaches 82.6 percent.

The effect was most pronounced on a dataset of difficult, ambiguous mentions: there the category-informed approach reached 71.8 percent while the specialised model dropped to 56.7 percent. Where things get ambiguous, a clear category assignment helps more than any amount of training.

For practice this is the central claim of this article: the category you belong to is not a marketing question but an attribution aid. Anyone who never states plainly what they are leaves that decision to a process with a known error rate.

What does omission as the most common error mean?

That the danger lies less in invented claims than in omitted context. A study by Haofei Xu, Umar Iqbal and Jacob M. Montgomery from May 2026 measured Google AI Overviews at scale: 55,393 search queries across 19 topic areas over 40 days, evaluating 98,020 claims decomposed into individual statements.

Two findings matter for your own presentation. First, 11.0 percent of individual claims were unsupported by the cited pages, and the dominant failure mode was omission, not false assertion. Second, source quality and claim fidelity proved largely independent of one another. A reputable source therefore does not guarantee that the claim derived from it is accurate.

What this looks like in practice is familiar to anyone who has read a summary of their own text. The sentence

Since specialising in 2024 we have developed phone assistants exclusively for medical practices.

easily becomes

develops phone assistants for medical practices

and at the next step

develops phone assistants.

None of these shortenings is a lie. The last one is worthless nonetheless, because it has lost the distinguishing feature. That is exactly how a description emerges that fits every second company in the industry.

How do you write sentences that survive omission?

By putting the distinguishing feature in the main clause rather than in a subordinate clause or a time reference. A sentence survives compression if it still holds true and still says something once everything subordinate is struck out.

The underlying rule can be checked in one move: strike everything after the first comma. Does what distinguishes you still remain? If not, the distinguishing feature sits in the wrong place.

This applies to your own website as much as to directory entries, profile texts and press releases. Which of those sources carry weight at all, and where mid-sized companies realistically get in, is covered in the article on the sources behind AI answers.

What does a model need in order to place you?

Six things, and they are unspectacular. This is not about a brand message but about the features that distinguish one entity from another.

1. Category: what you are, in common words. Not solution provider for digital transformation, but AI agency or software developer.
2. Service: what you concretely make or deliver, not what benefit it creates.
3. Audience: for whom, as narrowly as honesty allows.
4. Location: registered office and catchment area. For regional providers the strongest distinguishing feature there is.
5. Legal entity: the full company name including legal form, written identically everywhere.
6. Relationships: who you belong to, who you work with, where you hold membership.

The sixth point is the most frequently overlooked and matters most for affiliated companies. Anyone running two firms should describe the relationship identically on both sides, otherwise two entities emerge with contradictory details that devalue each other.

What if the company name is ambiguous?

Never let the name stand alone. A company name that exists several times over, or that is also an ordinary word, leads straight to the attribution problem the study puts at 75.4 percent. The model has to guess, and it occasionally guesses wrong.

Three measures help without requiring a name change. First, state the name consistently together with the category, so not Muster on its own but Muster GmbH, tax firm in Aalen. Second, keep the company name identical everywhere, including legal form and spelling. Third, make sure at least one easily discoverable page names all six features above in close proximity, so that they are captured together.

Whether this work pays off can be measured. What a defensible measurement looks like, and why a single query does not suffice, is covered in the article on measuring AI visibility.

Why are contradictory details worse than sparse ones?

Because a contradiction actively obstructs attribution, whereas a gap merely leaves it incomplete. If your website carries one service description, the trade directory another and a platform profile a third, no average of the three emerges. What emerges is uncertainty about whether the same company is even meant.

The most common trigger is unremarkable: old pages still online. A subdirectory from before the repositioning, a PDF with the former service range, a directory entry from 2019. As long as two versions remain discoverable, the system decides which one counts, and it does not always decide in your favour. What to do when incorrect details are already circulating is covered in the article on false AI claims about your company.

How do you test your own clarity?

With a check that takes half an hour and needs no software. It has three steps.

Step one, the shortening test. Take the first three sentences of your home page and strike everything after the first comma in each. Read what remains. If no category, no service and no audience appears any more, your self-description is not omission-proof.

Step two, the comparison. Open your website, your most important directory entries and your profiles side by side. Note the six features for each source. Every deviation is a candidate for a contradiction, every blank a gap.

Step three, the outside test. Have someone who does not know your company write down, using only these sources, what you do and for whom. What that person cannot work out, a language model certainly will not. This step is the most uncomfortable and the most revealing.

Frequently asked questions

Why does ChatGPT describe our company so generically? Because nothing more specific emerges from the available sources, or because the distinguishing feature fell away during compression. Omission is the most common failure mode in AI answers; in the study of Google AI Overviews, 11 percent of individual claims were unsupported by the cited page, mostly through missing context.

Does publishing more text about us help? Only if it is less ambiguous. More text increases the number of claims available for compression, and with it the number of possible contradictions. Aligning the existing details and moving the distinguishing feature into the main clause is more effective.

How important is the exact company name? More important than it appears. Language models assign name mentions to the correct entity in roughly three out of four cases; varying spellings with and without the legal form make that attribution harder still. Consistent spelling across all sources is one of the few measures that costs nothing.

We run two companies. How should we handle that? Describe the relationship identically on both sides and state it explicitly. If the connection goes unmentioned or is presented differently, two entities emerge with contradictory details, which worsens attribution for both.

Is schema markup enough to become unambiguous? It helps but does not replace running text. Structured data supplies the category in machine-readable form, and it was precisely that category information which lifted accuracy in the studied attribution task from 75.4 to 81.1 percent. What gets quoted is still the text people read.

How long until a sharper presentation takes effect? With systems that search the web live, a few weeks once the changed pages have been recrawled. Anything drawn from a training state changes only with the next training cycle. Plan for a quarter before evaluating, and measure before and after with the same question list.

Conclusion

The common diagnosis is that AI systems portray companies incorrectly. The measurements show something else: they portray them vaguely, and precisely where the source material is vague itself. A quarter of name attributions miss, and the most frequent error in the answers is not invention but omission.

From this follows a working method that differs from classic public relations. The point is not to tell more about yourself, but to ensure that the shortest possible version of your description still holds true and still distinguishes. Anyone who passes that test will also be reproduced correctly by systems that compress aggressively.

Getting started takes half an hour: shorten three sentences, compare six features across all sources, have an outsider read them. What remains afterwards is the description you will be found by.

This article reflects the state of knowledge as of 16 August 2026.

Sources

• Gerard Pons, Besim Bilalli, Anna Queralt (Universitat Politècnica de Catalunya, May 2025): Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation, arXiv:2505.02737. Accuracy of 75.4 percent without and 81.1 percent with category hierarchy across ten datasets
• Haofei Xu, Umar Iqbal, Jacob M. Montgomery (May 2026): Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact, arXiv:2605.14021. 55,393 search queries, 98,020 individual claims, 11.0 percent unsupported by the cited page