When AI gets your brand wrong: correcting false and outdated facts in AI answers

When AI gets your brand wrong, the error rarely looks like an error: an assistant quotes last year's price, recommends a product you discontinued, sends a buyer to an address you left, or attributes a rival's history to you because the two companies share a name. Brand misinformation in AI answers is any statement about a company's verifiable facts, such as prices, products, locations, ownership or identity, that an engine presents as current but that is false, outdated or belongs to another entity. Correcting false and outdated facts in AI answers is possible only indirectly, because no brand can edit an engine's output; what a brand can change is the evidence the engine finds.

Checking AI output before your own team relies on it is a different problem, covered in spotting hallucinations and verifying AI output. This article concerns the opposite direction, engines repeating wrong facts about your business to your customers, and sets out the error types, the mechanisms behind them, the line between what a brand controls and what it does not, a four-step correction playbook built on documented mechanisms, and a table for verifying each fix.

What kinds of factual errors do AI engines make about brands?

Factual errors differ from unfavorable judgments. A sentence saying your support is slow is an assessment, possibly grounded in real complaints, and is the subject of our article on how AI describes your brand; a sentence placing your headquarters in a city you left years ago is simply false. The remedies differ accordingly: judgments change when experience and evidence change, while facts can be corrected once the wrong source is found.

The recurring categories are wrong prices or plans, discontinued products presented as current, outdated addresses, phone numbers or opening hours, confusion with a similarly named company, wrong founders, ownership or founding dates, and invented features or policies. The first five usually trace to identifiable documents; the last tends to arise where no authoritative page exists. Detecting them depends on tracking how your brand is named in AI answers on a fixed question set, since one-off checks cannot separate a persistent error from a transient one.

Why do engines get brand facts wrong?

Four documented mechanisms explain these errors, often in combination, and each implies a different place to intervene.

Stale or conflicting sources

Engines that search before answering are only as current as the documents they retrieve. In their EMNLP 2024 survey of knowledge conflicts, Xu and colleagues describe inter-context conflict, in which retrieved documents disagree, and note that they may contain updated and outdated information simultaneously. OpenAI's help documentation for ChatGPT search likewise warns that search results and citations can be incomplete, outdated or incorrect. An old price on a comparison site or a never-updated directory listing can therefore prevail over your current page in a given answer.

Knowledge cutoff versus retrieval

A model's built-in knowledge stops at a fixed point; OpenAI's documentation lists a knowledge cutoff for each model, for example 30 September 2024 for GPT-5, a model the documentation now marks as deprecated. Huang and colleagues, in a survey published in ACM Transactions on Information Systems in 2025, describe the factual knowledge embedded in a model as temporally bounded and liable to become outdated. ChatGPT may search when a question would benefit from current information, but an answer produced without searching has no source for facts that changed after the cutoff, and when retrieved text contradicts what the model learned, the resulting context-memory conflict can resolve either way.

Entity ambiguity

Sharing a name with a better-known entity is a measurable disadvantage in retrieval. Chen and colleagues (ACL-IJCNLP 2021) built AmbER sets, groups of entities that share a name together with queries about each, and found that the retrieval systems they tested were twice as likely to retrieve erroneous documents for the less popular entity. For a small company named like a large one, the retrieved documents may simply be about someone else; how engines model a company as a distinct entity is explained in the knowledge graph and your brand entity.

Hallucination and data voids

Huang and colleagues distinguish factuality hallucination, a discrepancy between generated content and verifiable real-world facts, from faithfulness hallucination, a divergence from the user's input. Google, in a May 2024 post by Elizabeth Reid, VP of Search, wrote that AI Overviews are built to show only information backed up by top web results and generally do not hallucinate in the way other LLM products might, attributing odd results instead to misread queries, nuances of language and "data voids", topics with little high-quality content. For a brand, a data void is concrete: if no page states your return policy, an engine may fill the gap from whatever it finds.

What can a brand control, and what can it not?

A brand controls its website, its structured data, its official profiles such as a Google Business Profile, and the documents it publishes. It can influence but not decide third-party sources, from encyclopedia articles and Wikidata items to directories, review platforms, press archives and comparison sites, each governed by its own editors and rules. It does not control the engines: which sources they retrieve, how they weigh them, what their models learned, or when changes reach answers. The playbook therefore works from the inside out, and any offer to "remove" a fact from AI answers on demand describes a mechanism that none of the engines' documentation describes.

You cannot edit an AI answer, but you can make the true version of each fact the easiest one for an engine to find, read and confirm.

Step 1: make the canonical facts unambiguous on your own site

Publish one clear statement of each fact: an about or company-facts page with legal and trading names, founding date, headquarters and service regions, current product lines and how pricing works. Mark discontinued products as discontinued on their own pages instead of leaving them looking current; how engines extract information from product and pricing pages is discussed in why AI skips your product page.

Structured data states the same facts in machine-readable form. Google's documentation says Organization markup can help it understand an organization's administrative details and disambiguate it in search results. The markup has no required properties and is recommended for the home page or a single page describing the organization; recommended properties include legalName, alternateName, address, telephone, foundingDate and sameAs, plus identifiers such as vatID, leiCode and duns, and Google advises using the most specific schema.org subtype that fits. Schema.org defines sameAs as the URL of a reference page that unambiguously indicates the item's identity, which makes it the natural tool against name confusion. Google's guidelines add that markup must truly represent the visible page content and be up to date; details are in our guide to schema structured data markup.

Corrected pages also have to be reachable: a fact that crawlers cannot read corrects nothing, which is what an AI readiness audit checks. Google recommends the URL Inspection tool in Search Console to request a recrawl of individual URLs and a sitemap for larger sets, says crawling can take from a few days to a few weeks, and states that a request does not guarantee inclusion.

Step 2: fix the third-party sources engines read

• Google Business Profile. Owners edit their profile directly on Search or Maps, and Google reviews changes before publishing them; a business that moves after verification must verify again. Google may also update profiles from user reports and licensed content: owners have four days to accept or reject suggested edits they are notified about, and unanswered suggestions may be published if supported by other public information such as the business website, one more reason to keep your own site consistent. Setup is covered in how to set up a Google Business Profile.
• Knowledge panels. Google describes a "Claim this knowledge panel" process through which verified representatives can suggest changes, notes that not all panels can be claimed yet, and directs local businesses to Google Business Profile instead.
• Wikipedia. English Wikipedia's conflict-of-interest guideline strongly discourages editing articles about your own organization directly; the accepted route is to disclose the connection and propose corrections on the article's talk page with the {{edit COI}} template. Paid editors must disclose employer, client and affiliation under the Wikimedia Terms of Use.
• Wikidata. Under the notability policy, an item qualifies if it meets at least one of three criteria, one of which is that it refers to a clearly identifiable entity that can be described using serious, publicly available references. Wikidata has no formal conflict-of-interest policy, but its self-promotion essay strongly discourages creating items about your own organization, notes that Wikidata does not control Google's knowledge panels, and, where an item already exists, allows correcting clear factual errors as long as sourced claims are not removed, while other changes should be requested on the item's talk page with support from reliable sources. The wider context is in Wikipedia, Wikidata and how AI learns your brand.
• Directories, reviews, press and comparison pages. Send publishers the evidence and ask for corrections; annotate outdated releases in your own newsroom rather than leaving them to contradict current pages.

Step 3: use the engines' feedback channels without overestimating them

Each major engine offers a way to flag a bad answer, but none of their help pages describes a formal correction procedure for businesses. Google's help center describes thumbs-up and thumbs-down icons on each AI Overview, with "Report a problem" after a thumbs-down or via the three-dot menu; the feedback includes the latest query and its results and, in Google's words, helps improve AI Overviews for everyone. OpenAI points ChatGPT users to the thumbs-down icon or to Report for content that may violate its terms or applicable law, and Perplexity asks users to flag incorrect answers with the icon below an answer or through a support ticket. These are signals to the provider, not edits.

Personal-data law offers a narrower route, and only for individuals. OpenAI's privacy policy lets individuals request correction or removal of inaccurate information about themselves, considered against applicable law and the technical capabilities of its models. The GDPR right to rectification, however, belongs to data subjects, who are natural persons, and Recital 14 excludes personal data concerning legal persons, including a company's name, legal form and contact details. A founder misdescribed by name may have a personal-data claim; a company's wrong price does not.

Which error needs which fix, and how do you verify it?

Error type	Probable cause	Corrective action	How to verify the fix
Wrong price or plan	Old pricing pages, outdated comparison sites or reviews	One current, dated pricing page; retire or update old pages; ask comparison sites to update	Price questions return current terms; citations shift to current pages
Discontinued product shown as current	Product pages or reseller listings still live	Mark the product discontinued, name the successor, update listings	Repeated runs mention the discontinuation or the successor
Old address, phone or hours	Outdated Business Profile, directories, old press	Edit the profile (re-verify after a move); update directories and Organization markup	Profile shows new data; location questions answer correctly for several weeks
Confusion with a similarly named company	Entity ambiguity and popularity bias in retrieval	Disambiguating description, legalName, sameAs links, consistent profiles	Brand-name questions return your category and location; citations point to your domain
Wrong founder, ownership or founding date	Errors in encyclopedic or database entries, old articles	Correct your about page; request talk-page edits with reliable sources	Entry history shows the correction; answers follow it over time
Invented feature or policy	Data void: no authoritative page on the topic	Publish explicit pages, including what you do not offer	Repeated runs stop inventing; your page appears among citations

Step 4: re-measure over time and judge persistence, not single answers

Define a fixed set of fact questions, such as where the company is based, whether it still sells a given product and what a plan costs, and run them on each engine on a regular schedule, recording the answer and its cited sources. A correction counts as established only when it persists across runs, because answers to the same question vary, a measurement problem treated in why AI answers change every time. None of the engines' help pages gives a timeline: Google's estimate of days to weeks concerns crawling, not AI answers, and built-in model knowledge reflects its cutoff, so later changes reach answers through retrieval or, eventually, through models trained on newer data.

The citations are the most informative part of each record. If the wrong document drops out and the answer corrects itself, the fix worked through retrieval; if the wrong document is still cited, the work at that source is unfinished. If no wrong source is cited yet the error persists, learned model knowledge or entity confusion is the likelier explanation, which argues for strengthening disambiguation and continuing to measure rather than rewriting the same page again.

Where does Rocketly fit, and where does it stop?

Rocketly GEO Suite includes Approved brand facts, where the team records approved facts such as name, location, product, price range and service region, versioned, with an evidence URL and validity dates. The Compare with approved brand facts check then tests whether an approved fact phrase appears verbatim in a given answer and reports found, not found or incomplete answer. Its limit is deliberate: it is a phrase check that does not judge answers true or false and does not detect wrong or outdated facts automatically, and a missing phrase does not prove an error, since an answer can state a correct fact in other words. Google AI Overview is measured weekly as standard; ChatGPT and Perplexity measurement is in a controlled rollout. To keep canonical facts beside weekly answer measurement, you can create an account and evaluate Rocketly GEO Suite, offered as a separate add-on.

Frequently asked questions

Can I ask ChatGPT or Google to correct a fact about my company?

You can flag the answer through each engine's feedback controls, and it is worth doing for clear errors. None of their help pages describes a formal correction service for businesses, however, so feedback should accompany, not replace, fixing the sources the answer relies on.

How long does it take for a correction to appear in AI answers?

None of the engines' help pages gives a timeline. Google says crawling can take from a few days to a few weeks after a recrawl request, and the answer may change later or not at all, so plan for weeks of repeated measurement rather than a fixed date.

Should I create a Wikipedia article or Wikidata item to fix errors?

Generally not. Wikipedia strongly discourages editing about your own organization and Wikidata's self-promotion essay discourages creating items about it; the accepted path is to request corrections on existing pages, with reliable sources and a disclosed connection.