Being Recommended by an AI That Describes You Wrong Is Worse Than Not Being Recommended
Most AI visibility programs optimize for mention rate. Mention rate is a reasonable first metric and an incomplete one, because it treats every mention as a win. A mention that says you do not support single sign-on, when you have for two years, is not a win. It is a lost deal that never became a conversation.
The four attributes that go wrong
Pricing. The most frequently wrong fact about SaaS products, for a structural reason: pricing changes often, old pricing is preserved in reviews, comparison articles and forum threads, and those sources outnumber your current pricing page. Models trained and retrieved over that corpus reproduce the average, which is your pricing from two rounds ago.
Feature support. Especially for features added recently. The model's picture of your product is built mostly from third-party content, and third-party content lags your changelog by a long way. Anything you shipped in the last year is a candidate for being denied on your behalf.
Compliance and security posture. SOC 2, ISO 27001, HIPAA, data residency. Wrong answers here are disqualifying and they surface at exactly the moment a security reviewer is doing a first pass.
Company facts. Ownership, funding stage, headcount, headquarters. Usually low stakes, occasionally not. An enterprise buyer told you are a two-person startup when you are forty people with institutional backing has a procurement problem you will never hear about.
Why correcting it is not like correcting a search result
There is no form to submit. You cannot file a removal request against a model's weights, and even where an engine retrieves live, it is retrieving from sources you do not control. The correction loop is indirect: change what the corpus says, and wait.
That makes the work slow and makes prioritization matter. Three things actually move it.
A canonical, crawlable source of truth. One page per high-risk fact category: a pricing page with real numbers in HTML rather than in an image or behind a form, a security page listing certifications with dates, a comparison page you maintain. Stated in plain declarative sentences, marked up with structured data, and dated. The goal is to be the most unambiguous available source on the question.
Third-party corpus hygiene. Your listings on review platforms, directories, Wikidata and the aggregator sites that get scraped constantly are, in aggregate, a larger input than your own site. Most companies update these at launch and never again. Auditing them is unglamorous and it is frequently where the wrong number is actually coming from.
Recency signals. Undated content is treated as indefinitely old. Dated, updated content gives an engine a reason to prefer it over a 2024 review. Put a visible last-updated date on the pages that carry facts about you, and make it true.
Measure accuracy as its own dimension
If your visibility tooling reports only mention rate and sentiment, you are missing this category entirely, because sentiment can be perfectly positive about an incorrect claim. Track a small set of factual prompts with known correct answers, and score the responses as correct, incomplete or wrong. Twenty prompts covering pricing, your top five features, compliance and deployment options is enough to surface the pattern.
Then treat wrong answers as a defect queue with owners and dates rather than a marketing observation. A confident, wrong, widely-reproduced claim about your product is a bug in the most widely deployed description of your company, and nobody else is going to fix it.