Question
Why Your Brand Is Getting Attributed Incorrectly by AI Engines
LLM answers 4 min read
When an AI engine gets your brand wrong, start by working out which of four causes is at work: it has confused you with someone else, it is working from outdated information, it is repeating a wrong source, or it is filling a gap with something plausible but invented. Which one applies decides the fix. And whether the same error appears in one engine or several tells you where to look first: an error shared across engines points to a shared source, while an error in only one points to that engine’s own retrieval or model.
Four common roots
Name confusion. If another company, product or person shares or resembles your name, an engine may merge facts about both. Google’s Organization structured data documentation says adding Organization markup to your home page “can help Google better understand your organization’s administrative details and disambiguate your organization in search results.” Disambiguation is the problem this markup addresses, at least for Google.
Outdated information. A model learns from data collected before its release, and that knowledge does not change until a new version is trained. If you renamed a product, moved, changed leadership or dropped a service, a model may still state the old facts. Researchers call the clash between what a model learned and what a current page says a context-memory conflict; a survey by Xu and colleagues on knowledge conflicts describes such conflicts as able to “significantly impact the trustworthiness and performance of LLMs.”
A wrong source repeated. If a widely read article, a directory listing or a reference page contains an error, engines that retrieve or learned from it can repeat it. The more places the error has been copied, the more consistently it can appear.
Invention. When asked directly about a name, models sometimes produce confident details that no source supports. In an Authoritas experiment, nine AI models were asked directly whether fake expert personas were recognized authorities, and the models “failed 29.5% of the time, sometimes confidently inventing career histories for people who don’t exist.” A real brand with thin public information can attract the same kind of filled-in detail.
One engine or many?
Check the same questions in several engines before deciding what to fix.
| Pattern | Likely cause | Where to look first |
|---|---|---|
| The same error in several engines | A shared source | Wikipedia and Wikidata, widely syndicated articles, major directories, your own old pages |
| The error in one engine only | That engine's retrieval or training | The pages that engine cites for the answer, if any; the age of its training data |
| The error appears only without links | Model knowledge from training | Public sources that existed before the model's training cutoff |
| The error appears with a cited link | A retrieved page | The cited page itself |
When an answer cites its sources, follow the links. A wrong statement attached to a specific page can be the fastest route to the origin. When an answer gives no links, the error may come from training, and the fix will take longer to show.
Fix the source, not just the symptom
- Correct your own pages. Old About pages, press releases, archived product pages and outdated bios on your site are sources too. Update or redirect them.
- Add and complete Organization markup on your home page so Google can distinguish your company from similarly named ones.
- Correct reference sources through their rules. Wikipedia’s conflict of interest guideline says editors with a conflict of interest “are strongly discouraged from editing affected articles directly, and can propose changes on article talk pages instead.” Request corrections there, disclose your connection and cite independent sources. If your company has a Wikidata item, check its basic facts as well, keeping to Wikidata’s notability and sourcing rules.
- Ask publications to correct errors. A corrected article stops passing the mistake to systems that retrieve it after the correction.
- Fix directory and profile listings. Make your name, address, leadership and description consistent everywhere you control them.
- Strengthen the public record. If engines are inventing details because little is known about you, accurate independent coverage gives them something correct to draw on.
Report errors to the platform
Google offers a direct route for AI Overviews. Its help page on AI Overviews and feedback says that under each overview, users can click the thumbs-down icon and choose “Report a problem” for an answer that is “unhelpful, inaccurate, biased, or otherwise problematic,” and that the feedback includes the search query and its results. Google also says plainly that “AI Overviews can and will make mistakes.” Use the report option with a precise description of what is wrong and what is correct. For other engines, use the feedback controls each product provides.
How long corrections take
Corrections reach engines at different speeds depending on how the error got in.
- Retrieved pages update when they are recrawled. Google’s recrawl documentation says crawling can take a few days to a few weeks. OpenAI and Perplexity say robots.txt changes take about 24 hours to apply, but content changes still depend on their crawlers revisiting the page.
- Model knowledge changes only when a model is retrained or updated. An error learned in training can persist in that model even after every public source is fixed.
Test the same questions in several engines, follow any cited links to the source of the error, and fix it where it lives. Errors that come from retrieved pages can clear within weeks of a correction. Errors learned in training clear more slowly, which is a strong argument for keeping the public record about your brand accurate before a model learns from it.