AI Got Your Brand Wrong: How to Find and Fix Bad Information

Bottom line first: When AI gets your brand wrong, there are only two reasons: the source was wrong to begin with, or the source was right but got buried under outdated information. To decide whether an error is worth fixing, check two things: does the same question get the same wrong answer three times in a row from the same entry point, and is there a source in the citations you can actually edit? Only act if both are true.

1. There are only two reasons AI gets your brand wrong

AI doesn’t invent facts about your company out of thin air. Almost every sentence it produces traces back to a page it has read. So there are only two causes, and they call for completely different fixes.

Cause	Performance	Fix first	Typical time to results
The source is wrong	Your website, directories, or platform pages say something incorrect or outdated	Fix the source that gets cited most	4 to 8 weeks
The source is right but gets overridden	Your website is correct, but old news, old directories, or content about a same-name company is older or more plentiful	Add verifiable new facts to outweigh the old information	8 to 12 weeks
No source available	AI can’t find an official explanation, so it guesses	Add facts to your About, product line, and contact pages	6 to 12 weeks
Sources conflict	Three platforms say three different things, and AI picked one	Align your story first, then talk about correcting it	8 to 12 weeks

Of the four causes, the first is the easiest to fix; the second and fourth take the longest. The test is simple: check every fact in the AI answer against your website, one by one. If your website is right and AI is wrong, it’s the second cause, and changing your website won’t help.

AI answers are random. Ask the same question twice and the wording, or even the facts, can differ. One wrong answer proves nothing. You need to ask three times in a row from the same entry point with the same wording, then see whether it’s consistently wrong.

2. How to trace and fix the four types of errors

You can’t just call it all “AI making things up.” Once you split errors into four types, each one has its own tracing priorities and its own fix.

Error type	Typical example	Where to trace	How the fix differs
Factual error	Wrong founding year, factory address, headcount, or product line	Check your website and industry directories first	Correct the source file directly and keep a record of the change
Outdated	Old pricing, discontinued models, old phone numbers	Check old news, old directories, historical pages	Update rather than delete; keep the original URL and note the update
Confused	Mixed up with a same-name company or a competitor	Check same-name entities, competitor content, industry rankings	Add details that distinguish your entity and reinforce its unique identifier
Exaggerated	Someone else’s capabilities, customers, or certifications attributed to you	Check competitor websites and industry media coverage	Add qualifiers and scope, and clarify when necessary

Of the four categories, “exaggeration” is the easiest to misjudge. Some exaggerations work in your favor, like AI saying you served a certain major client. You shouldn’t quietly leave this kind of information in place: it creates false expectations on the customer side, and explaining it away at contract signing is costly. Exaggerations about certifications and qualifications can also create compliance issues. The test is whether the information would be verified in a real transaction. If it would be verified, you have to deal with it.

The principle for handling “outdated” content is the opposite of common sense: don’t delete old pages. Old pages carry accumulated authority and backlinks; deleting them throws away the entry points too. The right approach is to update the content at the original URL, note the update date, and replace outdated prices, model numbers, and contact details.

3. Discovery: add 8 to 12 brand fact questions to a fixed question set

The first step in correcting errors is being able to find them. Asking AI scattered questions is pointless; you need fixed questions, fixed entry points, and a fixed time window so results are comparable.

Keep brand fact questions to 8 to 12, and each one must have a right or wrong answer:

Question category	Example phrasing	Test	Suggested count
Identity facts	When was this company founded	Matches the business license	1 to 2
Location facts	Where is this company’s factory	Match the contact page on the official site	1 to 2
Capability facts	Does this company make a certain type of product	Match the product line	2 to 3
Scale facts	Employee count, factory area, production capacity range	Ranges are allowed, but not wrong by an order of magnitude	1 to 2
Market facts	Which regions do you mainly export to	Match the markets page on the official site	1 to 2
Qualification facts	What certifications do you have, and are they valid	Match the certificate numbers	1 to 2
Contact facts	What are the contact email and phone number	Match the contact page on the official site	1 item
Negative questions	Does this company make a certain type of product it actually doesn’t make	Answering “yes” is an error	1 to 2

“Negative questions” are the most useful and the most often overlooked. When information is thin, AI tends to expand outward and assign work from related categories to you, and only negative questions can catch that.

Score each answer on the four levels below; the proportions across the four levels are themselves a metric:

Level	Criteria	Counts toward accuracy
Accurate	Fully consistent with the official site fact table	Counts
Partially accurate	Main subject correct, details wrong, such as the year off by one	Counts, tracked separately
Mistake	Key fact wrong, or claims a capability that doesn’t exist	Counts
Can’t be judged	Answer is vague, or the model refuses to answer	Doesn’t count, recorded separately
No answer	Brand not mentioned at all	Doesn’t count, but mention rate should be recorded

Keep the original answers when scoring. When you look back in three months to ask “has it improved?”, you can’t compare without the original text. Retention can be text plus screenshots, with the question time and entry point in the same view.

4. Tracing: work backward from the citation list to find which layer of source is wrong

After finding an error, don’t fix it based on gut feeling. First look at the cited sources in the AI answer, list them, and group them by layer.

Source layer	Typical sites	Credibility	Can you change it	Typical time to results
Official Website	Your main site and sub-sites	Highest	Yes, change it yourself	4 to 8 weeks
Industry directories and B2B platforms	Industry association directories, B2B platform storefronts	High	Mostly yes, but you need to claim the listing or contact support	4 to 8 weeks
Third-party media and news	Trade press, trade show coverage	Medium	Yes, via a correction request	8 to 12 weeks
Forums and Q&A	Industry forums, Q&A communities	Low	Hard, you can only publish new content to push it down	12+ weeks
Competitor and same-name company content	Competitor websites, pages for companies with the same name	Not applicable	No	You can only outrank it with your own content

The traceability log needs to become a table:

Field	Purpose
Question ID	Maps one-to-one to the question set
Verbatim AI answer	Basis for the call and the baseline for retesting
List of cited sources	Decides which item you fix first
Source layer classification	Shows how hard the fix is and how long it takes
Error type	Factually wrong, outdated, confusing, exaggerated
Action taken and owner	Keeps corrections from becoming nobody’s job
Retest date	You must revisit it when the date arrives

With this table, fixing errors stops being “the AI is making things up” and becomes something you can schedule. The layered content approach fits right in here: rewriting commitment-layer sentences as fact-layer sentences lowers the odds of being misquoted. For details, see /insight/11-geo-content-layering-fact-opinion-claim/.

5. Prioritizing fact sources: fix the most-cited one first

The most common mistake is finding out the AI got something wrong and immediately editing your own website. If the AI isn’t citing your website, that’s wasted time.

Order your source fixes by these three rules, and don’t shuffle them:

Priority	Rationale	Action
First	Appears in multiple cited sources, or is cited most often	Fix this one first, it pays off the most
Second	Sources you directly control, such as your website and claimed platform storefronts	Update them together so the wording stays consistent
Third	Sources that need a third party’s cooperation, such as media outlets and association directories	Submit a correction request and accept a longer timeline

The more fundamental prerequisite is having a fact table. When the same fact is written three ways on your website, fixing any one of them is useless because the AI reads all three versions.

Fact field	Common inconsistency	Unified wording
Year founded	One place says year of registration, another says year the plant was built	Pick one definition and use only that sitewide
Company name	Chinese abbreviation, English name and registered name used interchangeably	Fix on one public-facing name sitewide, and label the registered name separately
Plant address	Registered address, office address and factory address used interchangeably	Label them separately, with the label stating the purpose
Headcount	One place says 200 people, another says 200-plus	Standardize on a range, such as 180 to 220
Product lines	Directory categories don’t match what the About page says	Use a single product line list
Main markets	One place says Europe, another says German-speaking countries	Standardize at the regional level, and list countries where needed

This table looks like busywork, but it decides whether your corrections hold. When the wording isn’t consistent, the spot you fix today gets polluted again by another spot next month.

6. Four actions on your website, three on third-party sources

Fixing sources runs on two tracks. You fully control the website track, so get those actions right the first time; the third-party track takes time to negotiate, so push it forward in batches.

Four actions on the website side:

Action	What we do	Test
One consistent set of facts	Build a fact sheet and write the whole site from it	One wording per fact across the site
Structured Data	Add structured markup to company info, products and certifications	Markup matches what’s visible on the page
Fill out the About and Contact pages	Add founding year, factory locations, headcount, main markets and contact details	All five fact types in place, each quotable on its own
Update old pages instead of deleting them	Update outdated content at the original URL and note the revision date	Old URL still works, content now current

Three things to do on third-party sites:

Action	Who to contact	Typical timeline
Claim and correct directory and platform listings	Industry association or B2B platform support	1 to 3 weeks
Request corrections to media coverage	The editor or the reporter who wrote it	2 to 6 weeks
Align social profiles and bios	Your own account dashboards	Done within a day

Of the three, the third gives you the most for your effort. Company bios, founding year and address are often written differently on social media, video channels and marketplace stores. Aligning them once removes conflicts across several sources at the same time, and it takes half an hour. The more consistent your third-party information, the easier it is for AI to see your brand as a clearly defined entity.

7. Retesting and limits: how long it takes, and when to stop

Fixing something doesn’t mean it takes effect. Search engines need time to recrawl, and model training data updates on an even longer cycle. With both in play, a 4 to 12 week observation window is realistic.

When to retest	What to check	What it means
1 week after	Has the site been recrawled?	If not, check indexing and your sitemap
4 weeks after	Have citations switched to the new page?	If yes, you’re on the right track
8 weeks after	Have answers to the same question improved?	Improvement means it’s working
12 weeks after	Accuracy across all brand fact questions	Compare with the baseline and calculate the gain

Retest results fall into three cases, and each calls for a different next step:

Scenario	Possible Cause	Next steps
Partial improvement	Your edits only covered some of the cited sources	Move on to the second-highest priority fix
No change at all	The sources actually being cited haven’t been touched yet	Recheck the citation list; don’t fix the same spot twice
Answers got messier	Old and new information now coexist, with conflicts across sources	Stop adding content and align your wording first

A few limits worth stating. First, AI answers vary: the same question can get different answers on different platforms, so one bad answer isn’t cause for alarm. Only consistently wrong answers are worth acting on. Second, some errors can’t be fixed: errors from the model’s own inference have no source to edit, and errors on platforms you can’t claim require the other party’s cooperation. Third, stop correcting in three situations:

Situation	Rationale	Recommendation
The error barely matters	It’s a trivial detail customers never ask about	Note it and spend no effort on it
The source can’t be changed	The platform won’t accept a claim or correction	Cover it with your own content instead of resubmitting
The error comes from inference, not a citation	No matching source in the citation list	Add facts and verifiable information rather than editing a single answer

The last point is worth remembering: the goal of correction isn’t to make AI get everything right every time, but to make sure your brand facts are written one way everywhere AI can read them. Once the wording is consistent, the remaining differences are just normal model variance, and you don’t need to chase them. How your brand entity is recognized by AI is the upstream question for this whole process; if your site doesn’t even have a complete About and Contact page, fix that layer first. For rollout pacing and a review checklist, follow the delivery notes in /site/ or start with the method write-up at /expert/crater/, or send us the error list you’ve tested via /contact/ and we’ll classify the types together.

FAQ

How long after changes will I see results?

Usually 4 to 12 weeks. Search engines recrawling your site typically takes a few days to 4 weeks; model-side corpus updates take longer and aren’t under your control. If you changed your site but AI is citing third-party platforms, 12 weeks may still show no change, because the direction is wrong. To judge whether you’re on the right track, check whether the cited sources have switched to new pages 4 weeks after the change. That gives you a signal earlier than the answer itself.

What if the same question gets a different answer every time?

Fix the variables first, then judge. Use the same entry point, the same phrasing, and the same time window, and ask the same question three times in a row to see if it consistently gets it wrong. If only one of three is wrong and the wording differs, that’s usually normal model variance and you don’t need to act. Only if all three are wrong and the wrong facts are consistent does it mean the source has a problem, and that’s when you trace it. Don’t keep rephrasing to get “the answer you want” — that just gives you a pile of results you can’t compare.

What if a competitor’s name shows up in my answer?

First determine what type of error it is. If AI attributes a competitor’s capabilities to you, that’s exaggeration, and you need to clarify your own capability boundaries and scope of application; if it just lists them in the same category for comparison, that’s normal. In handling, don’t issue a statement denying them — instead, write out your own verifiable facts: model numbers, certification numbers, delivery records, applicable scenarios. Once fact density goes up, the chance of confusion naturally drops.

How do I handle old news being repeatedly cited?

Don’t delete old pages, and don’t expect to clear third-party reposts. There are three steps: update the content at the original URL and mark the update date; publish a new official statement for the same fact; and give current valid pricing, model numbers, and contact info on your About and Contact pages. Old information gets cited because it was the only readable source at the time. When new sources exceed it in both quantity and credibility, citations will naturally migrate.

I changed my site but AI still says the old thing. Why?

Most likely, the AI isn’t citing your own website. Go back to the citation list and see whether it’s citing an industry directory, a marketplace storefront, or an old news article. Fix the one that gets cited most first, then update your website to match. Another possibility: the page was changed but the search engine hasn’t recrawled it yet. You can submit the URL manually in the backend, then wait 2 to 4 weeks and watch. In neither case should you keep tweaking your website and hope for the best.

Should you hire a third party to fix brand errors?

You can run a first pass yourself, and it costs very little. Put together 8 to 12 fixed brand-fact questions, ask them three times in a row, and record the sources cited. One person can finish this in half a day. What genuinely needs outside help falls into two categories: claiming and correcting information across multiple platforms, and correction outreach on the media side. Both require connections and process experience. The test is whether anyone on your team can stay with it through the retest. If no one can, then even if you outsource it, make it a condition that they hand back a list you can retest.

Final thoughts

The most counterintuitive thing about correcting errors is this: spotting the problem isn’t hard, holding back from changing your website is. Most AI mistakes live on other people’s pages. See the cited sources clearly first, then decide what to change, and you’ll save yourself a lot of wasted effort.

What matters more is the thing that looks least like error correction: settling on a single version of your brand facts. Once the fact sheet is done, three of the four types of errors shrink on their own. The remaining type comes from model variance, and isn’t worth chasing.

One line to remember: it’s not scary when AI gets your brand wrong. What’s scary is when you have five versions of your own story and it picks the wrong one.