Ai Answers Getting Facts About My Company Wrong

• Wrong answers rarely start inside the model.
• Start by recording the exact prompt, platform, date and a screenshot of the answer.
• Once an error is confirmed, trace the pages the answer relies on.
• Prioritise the sources the engine retrieves for your category queries first.
• Not every wrong answer is the same problem, and treating them alike wastes effort. For instance, according to 5W PR's guide 'When AI Gets Your Company Wrong: 5 Fixes That Work', fixing wrong AI answers involves auditing what each AI platform says, tracing the pages those answers rely on, correcting them with consistent facts, adding independent coverage, and re-checking every month.

Wrong answers rarely start inside the model. They start in the sources the model learned from or retrieved. Columbia University's Tow Center tested 1,600 queries across eight AI search engines and found more than 60 percent of responses contained factual errors, according to SubscribePR. Stale training data, a bad page the engine trusts, confusion with a similarly named company and outdated directory listings account for most cases. A fact that appears only once in training data sets a floor on how often a model gets it wrong, according to OpenAI's 2025 research cited by 5W PR. Models also guess instead of admitting uncertainty, because training rewards guessing, as the September 2025 paper 'Why Language Models Hallucinate' describes. Google's help page concedes that AI Overviews 'can and will make mistakes,' according to Always On Ward.

Start by recording the exact prompt, platform, date and a screenshot of the answer. Then ask the same question three to five times across ChatGPT, Perplexity and AI Overviews, as Always On Ward recommends, before deciding how widespread an error is. Single spot checks mislead, because ChatGPT, AI Overviews and Perplexity do not return the same answers and responses shift as models update, per Semrush. Re-check monthly at minimum. Tracking across engines is where a platform such as Fastlook's Citation Analytics removes the manual load, since it reports where your brand appears in AI answers across all major engines in real time.

Log every run in one sheet so drift is visible month to month. According to OpenAI's 2025 research cited by 5W PR, facts appearing exactly once in training data set a floor on how often a model gets them wrong.

Once an error is confirmed, trace the pages the answer relies on. This matters because a brand's own sites make up only 5 to 10 percent of the sources AI search references, according to McKinsey's October 2025 report cited via 5W PR. A wrong price is often sitting on a review site, a partner listing or a two-year-old press release that the engine retrieved live.

Open each cited URL and note the date, the claim and whether it matches your current facts. Assistants assemble answers from training data and, for many questions, pages retrieved from the live web, so both layers need checking, as Always On Ward explains.

Prioritise the sources the engine retrieves for your category queries first. Next, address directory listings and third-party profiles, which usually matter more than anything you publish yourself. Outdated directory listings are a documented cause of wrong facts, according to SubscribePR. Then check pages that directly contradict your own site.

Correct each source once, using identical facts everywhere. Specifically, the engine should see agreement rather than conflict across pages. For example, a single mismatched price on one listing can undermine an accurate page elsewhere. Consistency matters more than volume.

Where you have no control over a page, add independent coverage that states the correct fact. That is one of the five fixes 5W PR describes. According to the September 2025 paper 'Why Language Models Hallucinate' by OpenAI and Georgia Tech, models guess when uncertain because training and evaluation reward guessing over admitting uncertainty, and the error rate on arbitrary facts is at least the share of facts appearing exactly once in training data.

Not every wrong answer is the same problem, and treating them alike wastes effort. According to MaxAEO, pricing, feature, category, company-detail, policy, citation and entity errors each need a different verification source. Specifically, entity errors occur when the engine confuses you with a similarly named company, and they are fixed by clarifying your identity on authoritative profiles.

Outdated pricing is fixed at the source where prices live. Missing or invented details usually need a canonical page that states the fact plainly, which Page Engine can publish to WordPress, Webflow or Shopify with structured data and llms.txt. Positioning gaps are a different intervention entirely, since the engine is accurately describing how you present yourself.

No platform offers a guaranteed edit button, and models cannot be forced to one permanent correction everywhere, as Unusual notes. However, corrections work by changing the evidence the engine weighs. This takes time and varies by platform.

Expect retrieval-based answers to shift as sources are recrawled. Facts baked into training data change only at the next model update.

Specifically, none of these steps can solve a fact that exists nowhere authoritative. The realistic goal is a steady reduction in error rate, not elimination. Build a monthly retest against a fixed prompt set to confirm the change is holding.

Related guides

• Getting Your Brand Cited by AI Answers: AEO Strategy
• Get Your B2B SaaS Cited in Perplexity Answers: A Practical
• Bulk Citation Submission Services Cost: 2026 Pricing