When AI describes your business wrong

A business can appear in AI answers and still lose. If the description is outdated — wrong services, wrong location, wrong positioning — the mention does the wrong work. The buyer moves on with a confidently incorrect picture.

That gap is what the Accuracy dimension measures: how well recorded descriptions matched approved business facts, when the evidence supports scoring it.

Approved facts, not vibes

Accuracy is not scored against our impression of a business. It is scored against an approved fact sheet — the client's own confirmed record of identity, services, attributes, and positioning. Recorded responses are then evaluated on whether their descriptions match: identity, offering, attributes, positioning, source consistency, and hallucination control.

This is why Accuracy is frequently reported as unavailable: without an approved fact sheet, there is nothing honest to score against. An unavailable result is not a bad score — it means the required evidence was not there, and we do not estimate around that.

Why inaccuracy is expensive in a quiet way

A wrong description rarely announces itself. Nobody gets an alert that an assistant described their practice as a different specialty, or cited a three-year-old service list. The cost shows up indirectly: buyers pre-qualified for the wrong thing, or pre-rejected for the wrong reason.

It is also worth separating two cases. A response that omits you is a Visibility question. A response that names you and misdescribes you is an Accuracy question. They have different fixes, which is one reason the four score dimensions are never averaged into one.

What remediation looks like

Accuracy findings point back to the public record the assistants appear to draw on: unclear service pages, inconsistent profiles, outdated third-party descriptions. Remediation proposes grounded changes to that record — delivered as a bundle, published by the client, verified by a later capture. Whether the descriptions then improve is a question for re-measurement, not a promise.

Frequently asked questions

What do you need from us to score Accuracy?

An approved fact sheet: your confirmed record of who you are, what you offer, and how you describe yourself. Without it, Accuracy is marked unavailable rather than guessed.

Is an unavailable Accuracy score a problem?

No. It is an honest placeholder. It usually becomes the first action item: assemble the fact sheet so the next measurement can score the dimension.

Can you fix what AI says about us?

We can identify where the public record likely feeds the incorrect description and propose grounded changes. We cannot promise what any AI system will say after those changes — only verify what was published and re-measure later.

This note is for information only. Logres measurements describe recorded AI responses for a defined set of questions at a point in time; they are not a guarantee that any AI system will name, cite, or recommend a business in the future, and they do not predict rankings, traffic, leads, or revenue.