Why AI describes your business incorrectly.
Most wrong answers are not inventions. They are an accurate reading of inconsistent evidence, and the inconsistency is usually ours.
Why does AI describe my business incorrectly?
The instinctive explanation is that the model made something up. Sometimes it did. Far more often it read what is publicly available about the business, found several versions, and resolved the conflict in a way nobody would have chosen.
That resolution is the visible symptom. The cause is entity inconsistency: the accumulated set of public statements about who this business is no longer describes one business.
How it breaks, in order of how often we find it
- A rename that stopped halfway. The website and the primary profile carry the new name. An old directory, a supplier page, a sponsorship listing and an association membership carry the old one. Both are true statements about a company and only one is current.
- Two spellings that are the same company. Two words or one, an ampersand or an "and", a legal suffix present in some places and absent in others. To a person these are obviously the same. To a system resolving entities they are candidates.
- Categories that disagree. The business describes itself one way on its own site and is filed another way on the profiles that carry the most authority.
- Locations that multiplied. A moved office that was never removed, a suite number that appears in three formats, a service area described as a city on one surface and a region on another.
- Structured data that contradicts the visible page. The worst version, because it is a machine-readable statement that disagrees with the human-readable one on the same URL.
Why it is a tracking problem rather than a fix
Consistency is not a task that completes. It degrades every time the business changes, every time a platform adds a field, and every time somebody creates a profile in good faith without checking the exact form of the name. It is quiet, nothing errors, and there is no notification.
That is exactly the shape of thing worth watching continuously: cheap to check, slow to break, expensive to discover late. When a re-read shows a new spelling appearing or a category diverging, the change is small and the correction is small. Found a year later, after a dozen sources have copied the wrong version, it is a project.
What a correction actually looks like
One canonical description of the business, published where the business controls it, restated identically in the structured data on the same page, then reconciled outward across the profiles that matter, oldest and most-copied first. Where a third party will not change an entry, the goal shifts from correction to weight: make the correct version so much better evidenced that it is the obvious resolution.
None of that requires access to a model. It requires knowing which sources disagree, which is observable, trackable, and fixable.
This is one question beneath AI visibility tracking, the ongoing work Digilu does through The Observatory. The free point-in-time baseline is AIOInsights. Digilu cannot make a private AI model recommend a business. It can make the public evidence clearer, stronger and easier to verify, then track whether visibility improves.