AI Hallucination Audit: What AI Engines Are Making Up About You

The AI hallucination audit evaluates the accuracy of what AI engines currently say about your business when users ask about you directly. This is a fundamentally different evaluation from your technical AEO score or content quality. It measures the output side of the AI pipeline -what happens after AI engines have processed your content, competitors' content, web discussions about you, and their training data.

Here's what the audit does: it submits a battery of direct questions to multiple AI engines. "What is [your company]?" "What services does [your company] offer?" "When was [your company] founded?" "Who are [your company]'s competitors?" "What do customers say about [your company]?" Then it compares AI responses against your actual business facts to identify four categories of problems.

Hallucinated facts -things the AI invents outright. An AI might state your company was founded in 2015 when it was actually 2018, or claim you offer a product you've never sold. These fabrications typically occur when the AI has insufficient training data and fills gaps with plausible-sounding but incorrect information. Outdated information -facts that were once true but aren't anymore. Old pricing, discontinued products, former leadership, previous addresses. Competitor confusion -the AI mixes up your company with a similar one, attributing a competitor's features, pricing, or history to you. Missing information -important facts AI should know but doesn't mention at all.

Each error type requires a different remediation strategy, which is why the audit categorizes them precisely. Hallucinated facts need stronger entity signals. Outdated information needs content updates. Competitor confusion needs clearer entity differentiation. Missing information needs more prominent content creation.

There's no way to discover what AI engines say about your business without actually asking them. Your website's technical score, content quality, and schema markup are inputs to the AI's understanding -but the output, what the AI actually tells users, can diverge significantly from what you intended. The only way to audit this is to run real queries and evaluate real responses.

AI-level testing matters because hallucinations about your business are actively harmful. When a potential customer asks ChatGPT about your company and gets wrong pricing, fabricated founding story, or confused product descriptions -that misinformation shapes their perception before they ever visit your website. Unlike a negative review you can respond to, AI hallucinations are invisible to you unless you actively test for them.

The frequency of business-specific hallucinations is surprisingly high. In our testing across the customer support vertical, we found AI engines hallucinated at least one significant fact about every single company tested. LiveHelpNow (52) had incorrect founding year information in one engine and confused product descriptions in another. HelpSquad's service descriptions got mixed with a similarly named company in two out of three engines. Even well-known companies like Zendesk had outdated pricing and discontinued features appearing in AI responses.

The hallucination audit is also diagnostic -it reveals root causes of misinformation. The culprit behind a hallucinated founding year? Your website doesn't state the founding year prominently enough (or at all) in structured data. Competitor confusion? Your entity differentiation is insufficient. Each hallucination type points to a specific content or schema gap that, once fixed, reduces the likelihood of the error recurring.

The audit begins by assembling a ground truth profile of your business from your website's structured data, content, and any external knowledge base entries. This profile includes factual claims you make: founding date, location, services, pricing, team size, key people, awards, client testimonials, and other verifiable facts. This ground truth becomes the benchmark AI responses get measured against.

Next, the system constructs a query battery -15-25 questions probing different aspects of your business identity. Basic identity ("What is [company]?"), services ("What does [company] offer?"), history ("When was [company] founded?"), differentiation ("How does [company] compare to [competitor]?"), reputation ("What are the pros and cons of [company]?"), and specifics ("What is [company]'s pricing?"). Questions are phrased the way real users ask -natural, conversational, without the precision that would lead the AI to a specific page.

Each question gets submitted to ChatGPT, Claude, and Perplexity. Responses are captured in full. The system then performs clause-by-clause analysis of each response, comparing every factual claim against the ground truth profile. Each claim is classified: accurate (matches ground truth), hallucinated (contradicts or fabricated), outdated (was once accurate), competitor-confused (belongs to a different company), or missing (expected fact not mentioned).

The analysis produces a per-engine error profile. You might find ChatGPT hallucinates your pricing but gets services right, while Claude gets pricing right but confuses your founding story with a competitor's. These engine-specific patterns inform remediation because different engines weight different signals.

The report also tracks hallucination severity. Incorrect founding year -low severity (unlikely to affect business decisions). Incorrect pricing or fabricated features -high severity (directly impacts potential customers). Missing information about core differentiators -medium severity (the AI doesn't say anything wrong but omits what makes you valuable). Severity classification helps you prioritize which hallucinations to fix first.

Above 80: AI engines have an accurate, current, and reasonably complete understanding of your business. Minor errors may exist (slightly wrong founding year, incomplete service list) but no high-severity hallucinations. AI engines are reliable representatives of your business when users ask about you.

Between 50 and 80: a mix of accurate and inaccurate information across engines. This is the most common range for established businesses -core identity is recognized but specific details are wrong or missing. Start here: add clear, unambiguous factual statements to your homepage and about page -founding year, service list, pricing ranges, geographic coverage -in both visible content and Organization schema.

Below 50: AI engines have a fundamentally incorrect or incomplete understanding. The culprit is usually one of several things -your business is relatively new and hasn't built sufficient web presence for AI training data, your business name is similar to another entity causing persistent confusion, or your website lacks structured data needed for an accurate entity model. Remediation at this level is structural -you need baseline entity signals before worrying about content optimization.

Competitor confusion errors deserve special attention because they're self-reinforcing. Once an AI engine confuses your company with a competitor, users interacting with that incorrect information may produce new content (reviews, social posts) perpetuating the confusion. Addressing it requires strong entity differentiation: unique schema identifiers, distinctive sameAs profiles, clear differentiation statements, and a Wikidata entry establishing your entity separately from similar companies.

Track the hallucination audit across time. After remediation, re-run the audit 30-60 days later. AI engines don't update instantaneously -they recrawl, retrain, and refresh on different schedules. ChatGPT may correct a hallucination within weeks while Claude may take longer depending on crawl cycles. The longitudinal tracking tells you which remediations are working and which need further reinforcement.