Can I make AI stop saying wrong things about my company?

You cannot order a correction, but you can outvote the errors. AI systems fill gaps with whatever public data exists: directories, competitor pages, old press releases. The fix is publishing consistent, structured, verifiable data about your company so the correct version is the easiest one for AI to find and trust. In the remediation work we have run, fixing data consistency has preceded a decline in inaccurate descriptions over the following weeks as models re-index. Timelines vary by engine and category.

Do AI errors about my company actually cost me business?

Yes, but invisibly. When AI tells a buyer your company is three times larger than it is, does work you stopped doing in 2023, or omits your core product line, that buyer self-qualifies out or self-qualifies wrong. You never see the meeting that never got scheduled. That is what makes AI errors costly in a way bad search rankings are not: the loss happens before you can measure it.

Does a bad AI audit result mean my website is broken?

No, and that distinction matters. Your website can be well designed, load fast, and convert search traffic well, while AI systems still describe your company incorrectly. AI readiness is a separate discipline from web performance. The audit tests what AI systems say about you, not how your site scores in Lighthouse. Companies scoring below 35 on our AI readiness scale include sites that win design awards.

What is Generative Engine Optimization (GEO)?

GEO (Generative Engine Optimization) is the practice of adjusting website content to increase the likelihood that AI systems like ChatGPT, Gemini, and Perplexity will mention your company in their answers. It covers tactics like adding citations, structuring data for machine readability, and tuning content for passage-level retrieval. The problem isn't that GEO is wrong. It's that it's incomplete. Tuning content for AI pickup treats the symptom (you're not appearing in answers) without diagnosing the cause (your data signals are weak, inconsistent, or missing). AI systems evaluate trust and authority across your entire digital footprint, not just page formatting. GEO is a tactic. AI readiness is a system.

When AI Gets Your Company Wrong: A Field Guide

AI is telling buyers a story about your company right now. Across the diagnostics we’ve run at Corlarity, that story is wrong more often than it’s right.

We’ve assessed companies scoring below 35 out of 100 on our AI readiness assessment. Kitagawa-USA, a CNC workholding manufacturer in Schaumburg, Illinois, scored 30. Baker Products, a sawmill equipment manufacturer in Ellington, Missouri, scored 28. These aren’t outliers. They sit near the median.

When we run an AI Comprehension Check, a diagnostic model reads your website and describes what it found. The results follow a consistent pattern. AI gets the core business wrong, invents capabilities, confuses companies with competitors, serves stale information, and can’t answer specific buyer questions.

This isn’t a minor inaccuracy. It’s a structural problem with how AI systems understand most B2B companies. And it’s costing real revenue.

Here are the five categories of errors we see in nearly every diagnostic.

AI Miscategorizes the Core Business

The most common error is also the most damaging. AI identifies a company as something adjacent to what they actually do.

Baker Products manufactures industrial band resaws and sawmill equipment. Their homepage says “The Baker Difference” and “Built to Last.” During our diagnostic, we found their identifying tagline hidden in a commented-out HTML block. The tagline names them a wood processing equipment manufacturer. It was invisible on the rendered page. The homepage never clearly states what the company makes. A visitor would need to click through to the Equipment section to learn this is a sawmill equipment company.

When AI reads that homepage, it categorizes Baker as a generic “industrial equipment” company. Nothing on the page contradicts that default. Buyers searching for band resaws get directed elsewhere. Baker’s Clarity Index came in at 41 out of 100. The Clarity Index is a composite score. It measures how accurately and consistently AI systems can understand and trust your business data. Forty-one is Moderate, not catastrophic. But the miscategorization means they’re invisible for the queries that matter most.

This maps to the Accuracy factor in the Clarity Index. That factor asks whether claims about your company are correct and verifiable across sources. When your own site is vague about what you do, AI fills the gap with whatever the category suggests. The Answer Architecture fails at the first gate, Extraction, because the core facts aren’t extractable.

AI Invents Products and Features You Don’t Offer

The second pattern is fabrication: AI describes capabilities the company doesn’t have.

This happens when a website is vague about specific capabilities. AI systems don’t tolerate ambiguity. When they can’t find a clear answer, they infer from the category.

We see this pattern across multiple diagnostics. A metalworking company gets described as offering welding services because AI infers it from the category. The company does CNC machining, not welding. But their website never states their specialization specifically enough to override the category default.

Google’s quality rater guidance describes this mechanism. When a page lacks specific, verifiable information, raters and the automated systems that mirror them default to what the category suggests. Your specificity level determines whether AI cites your company or replaces it with a generic category description.

This is the Hallucination Risk factor at work. When AI can’t verify what you do, it generates answers based on category norms.

AI Merges Your Company with Competitors

Competitor confusion is the third pattern. It’s more common than companies expect. AI systems don’t read your website in isolation.

When two companies share a distributor, serve the same region, or occupy the same product category, AI regularly merges their attributes. Company A’s capabilities get attributed to Company B. Company B’s certifications appear on Company A’s profile. We’ve seen diagnostics where AI described a company’s product line using a competitor’s specifications.

Kitagawa-USA manufactures CNC workholding chucks with 1/1000mm precision tolerances and a 20-to-25-year average chuck service life. Their top competitors include Schunk, Buck Chuck, and Forkardt. When we ran problem-solution queries, we asked AI to connect buyer pain (parts moving during machining, high reject rates) to Kitagawa. The score was 0.0 out of 3.0. Zero. AI didn’t mention Kitagawa at all. It recommended Schunk and other competitors because Kitagawa’s problem-solution content wasn’t extractable.

When AI can’t distinguish your company from the category, it gravitates toward the dominant name. This maps to the Consistency factor in the Clarity Index. That factor asks whether your core data clearly differentiates you from adjacent companies. The information that should separate you includes specific tolerances, proprietary systems, and documented outcomes. If it’s missing or too vague to create separation, AI treats you as interchangeable.

The company name problem compounds this. If your company name is similar to a competitor’s, or if you share a category with a larger player, AI blends the two.

AI Serves Stale Information

The fourth category is recency failure. AI references discontinued products, former leadership, old locations, and outdated specifications.

This happens because the Recency factor in the Clarity Index is weak for most B2B companies. Your site had a product page from 2021. AI’s training data included that page. You discontinued the product in 2024 and removed the page. But the cached version persists in AI’s knowledge base, and nothing on your current site overrides it.

We see this pattern repeatedly across diagnostics. A company rebranded two years ago, but AI still references the old name. A company moved facilities, but AI gives the old address. A company shifted from custom work to production runs, but AI still describes them as a custom shop.

The recency problem compounds because AI systems weigh freshness differently across sources. A three-year-old directory listing that no one has updated can still outrank your current website. That happens when your site lacks strong recency signals. As answer sets harden, the wrong information gets locked in. AI learns from its own outputs. Once a description enters the training corpus, it becomes the baseline for future answers.

Research from the Stanford HAI AI Index tracks how training data lag affects model accuracy across domains. The pattern is consistent. Models default to cached information when nothing fresh overrides it.

AI Gets the Category Right. It Still Can’t Answer Specific Questions.

The fifth pattern is the subtlest. AI correctly identifies what you do. It just can’t answer specific questions about it.

A buyer asks, “Do they work with titanium?” or “What tolerances do they hold?” AI has the category right but can’t find the specifics. The information exists somewhere on your site. But it’s buried in a PDF spec sheet. Or it’s hidden in a JavaScript-rendered product catalog. Or it’s structured in a way that blocks extraction.

Baker Products runs their entire product catalog on a JavaScript-rendered eCommerce platform. The product pages, specifications, and pricing exist visually for human visitors. But AI crawlers that don’t execute JavaScript see an empty shell. The entire commercial catalog is invisible to the systems buyers use for research.

This is the Specificity factor: your content is too vague, too buried, or too technically inaccessible for AI to cite confidently.

The eight factors that determine whether AI trusts your data all contribute to this problem. But Specificity is the one that most directly determines whether AI can answer buyer questions with your information or someone else’s.

Every Error Costs a Meeting You Never See

Every error is a lost meeting.

Gartner’s B2B buying research documents what most sales leaders already sense. Buyers complete the majority of their research before contacting a supplier. When that research happens inside AI systems, the buyer asks for a recommendation. AI describes your company incorrectly. The buyer filters you out. You never see the traffic. You never know you lost.

This is the invisible cost of Trust Debt. Trust Debt is the gap between what you believe your data says and what AI actually understands. It doesn’t appear in your analytics dashboard. It appears as missing revenue and deals that never enter the pipeline. It appears as ready buyers who chose a competitor because AI described that competitor more accurately.

The product line AI carries in memory determines which buyers find you. When the memory is wrong, the buyers go elsewhere.

The compounding effect makes this worse. AI systems learn from their own outputs. When AI describes your company incorrectly, that description becomes training data for the next cycle. Errors harden.

These Errors Are Fixable. First You Have to Find Them.

These errors are fixable, but you have to know what AI is getting wrong before you can fix it.

The Clarity Diagnostic runs the AI Comprehension Check against your company. It surfaces every category of error with specific examples, queries, and fixes. It also scores your Clarity Index across all eight factors, so you know exactly where the gaps are. Coverage and Conversion build on that foundation once AI can describe your business accurately.

The fix sequence is always the same. Clarity first. If AI can’t accurately describe your business, nothing else matters. You can’t build content for topics if AI can’t extract your basic facts. You can’t create Intercept Pages for misconceptions until you know what AI actually believes about you.

Clarity starts with eight factors: Context, Accuracy, Consistency, Specificity, Recency, Machine Readability, Rendering Independence, and Hallucination Risk. Each one is measurable. Each one maps to specific changes on your site and in your external data.

The companies that fix this first have an advantage that compounds. When AI describes them accurately, they appear in more recommendations. More recommendations produce more proof. More proof strengthens their position in the knowledge graph. The Proof Loop turns in their favor.

The companies that wait are watching the gap widen. Every day, more buyers ask AI for recommendations. Every day, AI tells those buyers a story about your company based on stale, vague, or incorrect information.

Run the diagnostic. Find out what AI is getting wrong. Fix the foundation.