Auditing your footprint means checking not whether a profile exists but whether it says the same thing your site says. Contradiction between sources is worse for an engine than silence.
A gap leaves a model with nothing and it hedges. A conflict leaves it with two sourced answers and no way to choose, so it picks one and states it confidently.
Most third-party audits stop at a checklist of profiles claimed. That tells you almost nothing, because the profiles were never the point. What matters is whether the facts on them agree with each other.
What are you actually auditing?
Agreement, on a short list of checkable facts. Not tone, not completeness, not whether the logo is current.
The reason is mechanical. Schema.org defines the sameAs property as the “URL of a reference Web page that unambiguously indicates the item’s identity,” and Google says organization markup “can help Google better understand your organization’s administrative details and disambiguate your organization in search results.” Both sentences are about resolving one identity out of several candidates.
Contradiction is the thing that stops that resolving. If your site says one founding year and a directory says another, a machine holds two candidate values for the same slot, and nothing in the markup tells it which to keep.
Why is a contradiction worse than a gap?
Because the failure modes are different in kind. A missing fact produces a hedge: the engine says it is not sure, or says nothing, and a reader treats that as an absence.
A conflicting fact produces a confident wrong answer, because the model has a sourced value to state. Nothing in the output signals that another source disagreed. That is worse in three ways: it reaches the buyer as fact, it leaves no trace you can find in your own analytics, and it compounds as the incorrect value gets repeated.
The engine hedges or omits. Recoverable, and the fix is to publish the fact somewhere retrievable.
The engine picks one and asserts it. Harder to detect, harder to correct, and it can outlive the page that caused it.
Which sources should you check?
Start with the ones an engine can actually reach and is likely to trust, rather than with the ones you have logins for.
| Source type | Why it carries weight | Typical contradiction |
|---|---|---|
| Review platforms | Structured, frequently crawled, and written about you rather than by you. | An outdated pricing tier or a category label you have since moved on from. |
| Comparison and alternatives pages | They answer exactly the questions buyers ask an engine. | Feature claims frozen at whenever the page was written. |
| Directories and registries | Stable identifiers that other sources reference. | Legal name, founding year, headquarters, employee band. |
| Your own docs and pricing | The retrievable version of the truth. | Facts stated only in a table cell or an image, so they cannot be lifted. |
You do not have to guess which of these an engine actually reads. Microsoft reports it: the AI Performance report in Bing Webmaster Tools shows “the key phrases the AI used when retrieving content that was referenced in AI-generated answers,” alongside citation counts per page. That covers Microsoft surfaces only, and it is the one free first-party read you get.
How do you run the audit?
Five steps, and the discipline is in step two. Auditing everything produces a list nobody actions.
Six facts is deliberate. A longer list turns a tractable check into an inventory exercise, and the facts beyond those six are rarely the ones an engine states confidently about you anyway.
What do you do about what you find?
Sort by whether you control the page. Your own pages are the fastest fix and the place to start, because a correct, retrievable version of the fact gives the engine something better than the conflicting one.
Third-party pages are a request rather than a project. Most review platforms and comparison sites will correct a factual error if you point at the source, and the ask is stronger when you cite your own public page rather than asserting the correction.
You cannot delete a wrong fact from a model. You can make the right one better attested than the wrong one.
Community threads and archives are the exception: read them, do not manage them. They tell you how the market describes you, which is useful, and attempting to edit them is visible and usually counterproductive.
How often is this worth repeating?
Quarterly for the audit, and continuously for the signal that triggers it. The trigger is a drop confined to a few prompts across several engines at once, which is the shape a shared source going stale makes, described in what sources does AI use for brand information.
Between audits, track the six facts as questions rather than as a checklist. Asking each engine what you charge and where you are based, on a schedule, surfaces a contradiction faster than re-reading profiles does, and it measures the thing you actually care about, which is what the engine says rather than what the directory holds.