# Why AI Gets Facts About Your Brand Wrong, and How to Fix It Author: Quentin Megevand Published: 2026-11-07 Source: https://getnamedrop.ai/en/blog/why-ai-gets-facts-about-your-brand-wrong-and-fixes AI engines get brand facts wrong because they predict likely text from training data and from pages they retrieve, so stale, conflicting, thin or misread sources turn into confident wrong answers. You cannot correct the model directly. The fix is to correct the sources the engines read, then re-test on a fixed prompt set to confirm the change. ## How AI engines build an answer about your brand An answer about your brand is not read from a record. The engine assembles it from several layers: what the model absorbed during training, the pages it retrieves when it searches, and the third-party sites those pages draw on. Your own website is one input among many. ### Training data versus live retrieval The source of an answer shapes every fix. An answer drawn from training data alone repeats what the web said when the model was trained, which can be months old. A price change or a new chief executive does not exist for that answer. An answer built on live retrieval comes from pages the engine fetches for the prompt, usually with citations. It can change as soon as those pages change, but it inherits every error on the pages it retrieves. When a wrong answer shows citations, start with the cited pages. When it shows none, the error probably sits in training data, and a corrected source will only show up after a model update or once the engine starts retrieving for that prompt. ### Why a wrong answer sounds as confident as a right one A language model does not store a verified table of facts. It predicts likely next words from the prompt and the text available to it. When the facts it needs are missing or contradictory, it still produces fluent text, and the gap is filled with a plausible guess stated in the same tone as a verified fact. Fluency is not a signal of accuracy. A wrong fact and an invented fact look alike to a reader but need different fixes. A confidently stated wrong fact usually has a real origin: an outdated directory listing, an old press release, or a retrieved page that ranks well and is wrong. You can trace and correct it. A fabricated fact has no source: the model invented a founder, a product or a price to complete the answer. Fabrication points to thin information, and the fix is publishing clear facts, not hunting for a page that does not exist. ## The causes of wrong brand facts, from stale pages to misread sources ### Outdated and conflicting third-party pages A primary cause is information about the brand that sits outside its control and was never updated. Directory listings, review sites, marketplaces, comparison pages, old news coverage and partner pages keep old prices, discontinued products, former leadership, closed locations and outdated opening hours. When sources disagree, the engine may pick either version or blend them into a fact that appears nowhere. ### Errors that cite your own content Your own site can be the cited source of an error. Profound's FactCheck analysis, published on 17 August 2026, found that "54% of brands had at least one inaccurate claim citing their own content", and called regular audits of owned media an immediate lever for improving accuracy. Typical culprits: a pricing page updated while an old plan comparison stays live, an about page listing a former team, a PDF brochure nobody remembers publishing. Check pricing first, since it changes often and errors cost sales; this priority is our inference, not a study finding. ### Thin first-party information the engine fills in When a brand publishes little about itself, the engine has little to retrieve and more to predict. A company without a clear about page, a public price list or a consistent description across its profiles leaves gaps, and gaps invite invented details. We infer that small and local brands face more of this than large companies, because fewer independent pages describe them; no study we cite measures the difference. ### Category and positioning misdescription Some answers get every fact right and still describe the brand badly: an accounting platform presented as a spreadsheet template, business software recommended to consumers. This is not a factual error, and correcting a fact will not fix it. It comes from how the category is described across the web, so the fix is consistent category language on your pages, in your profiles and in the third-party lists that include you. ## How to measure how often AI gets your brand facts wrong Customer complaints are a late and partial signal. Test AI answers against a verified fact sheet on a schedule instead. ### Build a fact sheet and a fixed prompt set List every checkable claim: legal name, founding date, leadership, locations, opening hours, products and plans, prices, integrations, certifications and category. Each line gets a value, a source URL and the date you last verified it. Then write prompts a real buyer would ask: what the brand does, what it costs, how it compares with named alternatives, where it operates. A couple of dozen prompts covering each fact type is a workable start. Freeze the wording, because a changed prompt makes runs incomparable. ### Record answer, citations, engine and date for every run Answers vary from run to run, so a single test is an anecdote, not a measurement. Run each prompt several times per engine and log the full answer, the cited URLs, the engine, the model or mode where visible, and the date. From the log, compute an error rate per engine: wrong claims divided by checkable claims. Compute it per fact type too, to see which facts drive the errors. For sample size, see [how many prompts and runs make a change real](/en/blog/ai-search-visibility-reporting-the-metrics-clients-need-in-2026). Track refusals separately from errors. Seer Interactive's study of AI answers about its own brand, published on 27 August 2026, reports: "When AI platforms actually answered a branded prompt about us, they got it right 95.9% of the time." The study's headline finding is that the engines often declined to answer. An engine can be accurate when it answers and still leave a buyer with nothing, so report accuracy and refusal rates together. ### Logged-in, temporary and fresh sessions Memory, chat history and personalisation can change how an engine describes a brand to a given user. An account used daily for work on the brand is a poor test context. Run measurements in temporary or logged-out sessions where the engine offers them, or in a fresh account with no history, and record the session type in the log. ## Correcting the sources: owned pages, entity data and third-party pages Do not start by arguing with the AI. Start by auditing every place the brand's facts appear and aligning them on a single source of truth, the fact sheet, with consistent names, values and category language. Then correct in order of control: owned pages, then entity sources, then third-party pages. ### Fix your own pages and keep structured data aligned with them Update or remove every owned page that contradicts the fact sheet, including old posts, archived pricing, press pages and downloadable files. Then check that structured data says what the visible page says. Google's structured data guidelines, checked on 7 November 2026, state: "Don't mark up content that is not visible to readers of the page." Markup that still describes a price, a founder or an address the page no longer shows is a contradiction you publish yourself. Our [GEO audit checklist covering entity facts and schema](/en/blog/geo-audit-checklist-12-checks-before-you-touch-your-content) lists these checks. ### Correct entity sources: Knowledge Panel, Business Profile, Wikidata We infer that entity records weigh on basic facts such as name, address and leadership. For a Google Knowledge Panel, the Get verified on Google page in Google Search Help, checked on 7 November 2026, starts with: "Search for yourself or the entity you represent and find your knowledge panel." Complete verification as the brand's representative, then submit corrections from the panel and keep a dated record of each request. For Google Business Profile, update hours, address, categories and attributes from the verified owner account, and close duplicate listings. For Wikidata, read the community's conflict-of-interest guidance before editing, disclose your affiliation, and back every statement with a reference to a public source. ### Get high-visibility third-party pages updated Before contacting anyone, trace the error to the exact page an engine cited. Open each cited URL, find the wrong sentence and note the date. If the answer has no citation, search for the wrong fact as an exact phrase to find the pages that carry it. The method for [how to trace an AI answer back to the page behind it](/en/blog/how-to-find-which-sources-feed-a-competitor-s-ai-mentions) applies here. Then start with the pages cited most often across your prompt set, and send each owner a short request with the correct value and a link to your source. Watch for seeded content: an unsigned comparison page, reviews repeating the same wrong claim, or a competitor's page showing your old prices. Document it with dated screenshots before asking the host site to correct it. ## What each AI engine lets you report, and how long a correction takes Platform feedback channels exist, but they shape future answers indirectly, and the durable fix stays in the sources. The table records only what we confirmed in vendor documentation on 7 November 2026. | Engine | Shows citations | Documented feedback route | Personal-data or legal request route | |---|---|---|---| | ChatGPT | Not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | | Perplexity | Not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | | Gemini and Google AI Overviews | Not documented, checked on 7 November 2026 | AI Overviews: a feedback route for an overview found "unhelpful, inaccurate, biased, or otherwise" (Google Search Help, checked on 7 November 2026); Gemini app: not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | | Claude | Not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | Not documented, checked on 7 November 2026 | When a wrong fact persists after the source is corrected, work through it in order. First, confirm the corrected page is crawlable and indexed: not blocked, not set to noindex, and serving the new content. Second, separate live-retrieval answers, which can change once the engine re-reads the page, from training-data answers, which wait for a model update you do not control. Third, re-run the same prompts. No vendor we checked publishes a time-to-fix, so any timeline is one you measure. Materially false or defamatory statements that cause real harm may give rise to legal recourse in some jurisdictions. That is a question for counsel, not a routine correction channel. ## Where to start this week: audit, rank and correct your brand facts 1. Write the verified fact sheet, with a value, a source URL and a verification date for each claim. 2. Run the fixed prompt set on ChatGPT, Perplexity, Gemini and Claude in clean sessions, several runs per prompt, logging answer, citations, engine and date. 3. Classify each error by fact type and rank it by severity: size of the accuracy gap, visibility of the prompt, and customer or regulatory impact. Pricing, legal, health and financial facts come first. 4. Trace each priority error to the page an engine cited or the pages that carry the wrong fact. 5. Correct owned pages, then entity sources, then third-party pages, logging each change with its date. Before closing the week, put a dated re-test in the calendar on the same prompt set, engines and session type. Keep every result: the log turns time-to-fix into a figure you measured rather than assumed. As a next step, [run a free Namedrop scan](/en?src=blog-article#scanner) to see what ChatGPT, Perplexity, Gemini and Claude currently say about your brand and which sources they cite. ## Sources - [Profound, Where do inaccurate AI claims come from? | Profound, consulted 2026-11-07](https://www.tryprofound.com/blog/where-do-inaccurate-ai-claims-come-from) - [Seer Interactive, Study: AI Declined to Answer Prompts About Our Brand 31% of the Time (But Got Our Info Mostly Right When It Answered), consulted 2026-11-07](https://www.seerinteractive.com/insights/ai-brand-accuracy-study) - [Google Search Central, General Structured Data Guidelines | Google Search Central | Documentation | Google for Developers, consulted 2026-11-07](https://developers.google.com/search/docs/appearance/structured-data/sd-policies) - [Google Search Help, Get verified on Google, consulted 2026-11-07](https://support.google.com/knowledgepanel/answer/7534902?hl=en) - [Google Search Help, Find information in faster & easier ways with AI Overviews in Google Search, consulted 2026-11-07](https://support.google.com/websearch/answer/14901683?hl=EN) ## Frequently asked questions ### Why does AI get facts wrong about my brand? AI engines predict likely text rather than look facts up. They answer from training data, which can be months old, and from pages they retrieve, which may be outdated, conflicting or thin. When facts are missing or contradictory, the model fills the gap with a plausible guess and states it as confidently as a verified fact. A confidently stated error usually traces back to a page you can find and correct. ### Can you correct what ChatGPT says about your company? Not directly. Feedback sent through an engine's interface affects future answers indirectly at best and does not edit a fact on demand. The durable correction sits in the sources: align your own pages and structured data with a verified fact sheet, update entity sources such as your Knowledge Panel and Business Profile, and ask owners of cited third-party pages to fix wrong details. Then re-test the same prompts to confirm the change. ### How long does it take for AI answers to reflect corrected brand information? No vendor we checked publishes a time-to-fix. Answers built on live retrieval can change once the engine re-reads the corrected page, provided it is crawlable and indexed. Answers drawn from training data wait for a model update, which you do not control. Treat the delay as something you measure: log the date of each correction and re-run the same prompt set on the same engines until the answer changes. ### Is AI more likely to get facts wrong about small brands? That is a reasonable inference, but no study we cite measures it. Small and local brands usually have fewer independent pages describing them, so engines have less to retrieve and more to predict, and gaps invite invented details. The remedy is the same at any size: publish clear, consistent facts on your own site and profiles, and get the third-party pages that mention you corrected.