How to fix wrong AI answers about your brand with GEO / AEO
A practical method to detect, diagnose and correct inaccurate ChatGPT, Gemini, Perplexity, Claude, Copilot or AI Overviews answers about a company.
A company can appear in an AI answer and still lose opportunities. The problem is not always lack of visibility. It is often the quality of what ChatGPT, Gemini, Perplexity, Claude, Copilot, Bing or Google AI Overviews says about the brand: outdated services, wrong locations, imprecise comparisons, incorrect links or recommendations that do not reflect the real offer.
In GEO / AEO, correcting a wrong answer does not mean “sending an instruction” to the model. It means improving the set of signals that generative engines and answer engines can retrieve, compare and cite. The website, structured data, sitemap, llms.txt, external sources, brand mentions and service pages need to tell the same story clearly enough.
GEO / AEO is the process of reducing ambiguity around a brand so AI systems can understand, cite and recommend it with fewer errors.
First: document the exact error
Before changing content, document what failed. It is not enough to say that “AI got it wrong”. Save the prompt, engine, language, answer, cited sources, omitted URLs and type of error. This record separates an isolated failure from a repeated pattern.
- Entity error: AI confuses the brand with another company, uses an old name or mixes domains.
- Offer error: it attributes services the company does not provide or ignores important services.
- Location error: it invents local coverage, address, country or language.
- Citation error: it links the wrong page, an outdated third party or a source that does not support the claim.
- Comparison error: it recommends competitors because it finds stronger external proof about them.
- Freshness error: it uses an old version of the website or a service page.
This stage connects directly with a GEO / AEO prompt portfolio. If a brand checks only one isolated question, it cannot tell whether it has a retrieval, citation or reputation problem. If it measures prompt groups by intent, it can prioritize the errors that affect business.
Second: locate the likely source of the failure
Generated answers often rely on a mix of web index, cited sources, retrieved content, structured data, prior knowledge and user context. That is why correction should look for the most likely cause. If AI links an old page, the issue may sit in sitemaps, redirects or internal links. If it cites an external directory with outdated information, the issue may be outside the website. If it misunderstands the service, the brand may be missing a canonical page with scope, limits and examples.
The useful question is not “why did the model invent this?”, but “which available source supports or disproves this claim?”. A solid AI visibility audit compares the answer with owned pages, external sources and technical signals that an answer engine can use.
Third: create a canonical page for the right answer
When an important claim has no clear URL to defend it, AI tends to reconstruct it from scattered fragments. For GEO / AEO, every relevant commercial question should have a canonical page or section: what the company does, who it serves, what the service includes, what it excludes, how it is measured, which languages it works in and how to contact the team.
This does not mean creating thin pages for every keyword variation. It means consolidating useful answers into assets that humans can understand and AI systems can reuse. A methodology page, a definition of what GEO is, a definition of what AEO is or a guide to citable content can act as internal sources when they are well linked and up to date.
If there is no clear page answering a question about the brand, a generative engine has to rebuild the answer from incomplete pieces.
Fourth: align visible and technical signals
The most useful changes are often simple, but they need to be applied consistently. The title, meta description, H1, visible text, internal links, schema, canonical, hreflang, sitemap and llms.txt should not point to different versions of the company. If a page says one thing and structured data says another, the website is creating noise.
- Update the page that should be cited, not only the blog.
- Add a short, self-contained definition that can be extracted without extra context.
- Link from strong pages to the URL that corrects the answer.
- Review GEO / AEO structured data for Organization, WebPage, Service, Article and breadcrumbs.
- Check that robots.txt does not block relevant crawlers and that the sitemap contains the correct URL.
- Maintain bilingual parity so an English answer does not rely on a non-equivalent Spanish source, or the reverse.
Fifth: correct external sources and brand mentions
Many AI answers are not formed from the owned website alone. Directories, social profiles, media, partners, marketplaces, repositories, public databases and third-party mentions can reinforce or contradict the entity. If those sources describe the company badly, the official site is surrounded by conflicting signals.
A mature GEO / AEO strategy builds a source map: which domains mention the brand, what they say, whether they link the correct URL, whether they use the current name, whether they describe services accurately and whether they appear in generated answers. Not every source has the same authority, but repeated and consistent sources help answer engines reduce doubt.
Sixth: measure whether the correction has been absorbed
The correction is not finished when a page is published. Re-run the prompt portfolio, review whether the right URLs appear and check whether tools such as Bing Webmaster Tools show citations or related queries. In some cases the change takes time to surface because it depends on crawling, indexing, external source recrawling or retrieval-system updates.
- Does the answer describe the company and services more accurately?
- Does it cite an owned URL or a reliable source that supports the claim?
- Has the old information disappeared, or does it only appear less often?
- Does the error repeat in one engine or across several?
- Do the Spanish and English versions answer equivalently?
- Does AI referral traffic land on pages that can actually convert?
What not to do
Do not fill the website with artificial copy written only for models. It also does not help to repeat the brand unnaturally, create invisible FAQs, mark up data that does not appear on the page or block search crawlers and still expect the brand to be cited. AI systems need access, clarity and proof, not contradictory signals.
It is also risky to treat GEO and AEO as an isolated reputation campaign. If the product, positioning or offer is unclear, AI will only amplify that confusion. Improvement should start with the operational truth of the company: what it does, what it does not do, what makes it different and which sources can prove it.
Conclusion: fixing answers means governing sources
AI visibility is not controlled with a single tag or a magic paragraph. It is governed through clear sources, canonical pages, coherent structured data, internal links, external signals and recurring measurement. When those pieces are aligned, errors do not disappear completely, but the probability that a generative engine understands and cites the brand correctly improves.
Blobic approaches GEO / AEO as a correction and growth system: we detect what assistants say about a brand, find the source of the error, strengthen citable pages and measure whether the answer changes. If you need to know what AI is saying about your company and which signals should be fixed first, the AI visibility audit is the most direct starting point.