AI describes your brand wrong when the sources it learned from conflict with each other or barely mention you at all. When your homepage, profiles, and third-party listings tell different stories, or go silent, the model fills the gap by guessing. It is a content and consistency problem you can fix at the source, not bad luck or a bug to report.
This is the flip side of entity SEO: a wrong description is almost always a fuzzy or contradictory entity. Here is how to find the cause and correct it durably.
Why AI Gets Your Brand Wrong
A model builds its picture of you from everything the web says. When those sources agree, the picture is confident and correct. When they disagree, or when there is barely anything to go on, the model does what it always does with uncertainty: it guesses, confidently.
Every hallucination about your brand is a signal that no strong, consistent source exists for that fact. The model isn’t malfunctioning; it’s filling a gap you left.
That reframe matters, because it means the fix is in your hands. You cannot edit the model, but you can change what it reads.
Diagnose the Cause
Ask each assistant to describe your company by exact URL, in a clean session, and note precisely what it gets wrong, the specific fact, not just “it’s off.” Then trace that fact back to where the model could have learned it.
A worked example
Say ChatGPT calls you a “marketing agency” when you’re a SaaS product. Search your brand and read the first page of results as the model would. You’ll often find the culprit: an old directory listing, a miscategorised profile, or a press piece that framed you loosely, and your own site never states the category plainly enough to override it.
The Three Causes, and How to Tell Them Apart
- Contradiction — your own pages state different facts (setup time, category, pricing) in different places, so the model can’t tell which to trust.
- Stale source — an old profile, listing, or article still carries outdated information the model is still reading.
- Absence — there simply isn’t enough about you for the model to be right, so it invents a plausible-sounding answer.
Match the wrong fact to its source. Almost every AI error about your brand traces back to a specific page you can find and fix.
Fix It at the Source
- 1Correct your own pages first. Make your homepage, About, and key pages state the right fact clearly and consistently. This is the source you fully control.
- 2Update every profile and listing. Directories, social profiles, review sites, anywhere the stale or wrong fact appears.
- 3Fix your structured records. Organization schema and your Wikidata entry are high-trust sources; getting them right carries weight.
- 4Publish an authoritative statement of the correct fact, clearly worded and easy to lift, so the accurate version is the most citable one available.
The Common Types of Error, and What Each Signals
Not all wrong descriptions mean the same thing, and the type of error points to its cause. Learning to read them saves time.
- Wrong category (“agency” when you’re a SaaS) signals that your own pages never state the category plainly enough to override a loose external source.
- Outdated facts (an old price, a former product name) signal a stale source still being read, often a directory or an old article.
- Confusion with another brand signals a disambiguation problem, the model has merged you with a similarly-named entity because nothing separates you cleanly.
- Invented specifics (features you don’t have, claims you never made) signal absence, there wasn’t enough real information, so the model filled the gap.
The confusion-with-another-brand case is worth special attention, because it is pure entity work: the answer is stronger disambiguation signals, a clear sameAs graph, a distinct description, and a clean knowledge-base entrythat separates you from the entity you’re being merged with.
How to Prevent It Happening Again
Most wrong descriptions are preventable with the same discipline that fixes them: keep one consistent story everywhere, and make the important facts impossible to miss.
- State your category plainly in your title, H1, and first paragraph, so there’s no ambiguity to fill.
- Keep profiles current as part of routine maintenance, not a one-off cleanup.
- Re-check after any change — a rebrand or repositioning is the highest-risk moment.
When It Still Won’t Correct
Sometimes you fix every source you can find and a model still repeats the old, wrong fact. This is frustrating but usually explainable, and there are a few remaining levers.
First, you may have missed a source. Search more broadly, including places you don’t control, aggregators, scraped copies, cached versions, and old profiles on platforms you forgot you were on. The wrong fact is being read from somewhere, and it is almost always findable if you look the way the model does.
Second, the error may be baked into a model’s training data, in which case live-retrieval assistants will correct once your sources are clean, but a training-based model may lag until it next updates. Prioritise fixing the highest-authority sources, the ones most likely to be re-read, and accept that some correction is a matter of patience. Most assistants also offer a feedback mechanism on individual answers; using it does not guarantee a fix, but it is worth doing for high-stakes errors while your source corrections propagate.
The Reputation Dimension: When AI Repeats Something Damaging
Sometimes the problem is not a neutral factual error but something reputationally harmful, an old controversy, a misleading framing, or a criticism that a model surfaces and repeats. This is more sensitive than a wrong category, and it deserves a slightly different approach, though the underlying mechanism is the same: the model is reflecting sources, so the durable response is to change what the sources predominantly say rather than to argue with the output.
The instinct to demand a takedown or dispute the model directly is understandable but usually ineffective, because the model is not the origin of the claim; it is a mirror. If the balance of credible, current sources tells a fair and accurate story, the model tends to reflect that balance over time. That means the work is to ensure your side is well-documented and well-sourced, to publish clear, factual information that provides the context an unfair framing omits, and to earn current, credible coverage that outweighs stale or one-sided material. This is genuine reputation work, and it is slow, but it is the only approach that holds, because it addresses the cause rather than the symptom.
There is an important line to hold here: the goal is accuracy and fair context, not suppression of legitimate criticism. Attempting to scrub every negative mention tends to backfire, both because it rarely works against the sheer volume of sources a model reads, and because a suspiciously spotless footprint is itself a signal. The realistic and honest aim is to make sure the true, current, well-documented version of your story is the dominant one, so that when a model synthesises what the web says about you, it synthesises something fair. As with every other error type, the fix lives in the sources, not the model.
How Long It Takes to Update
It depends on the system. Live-retrieval assistants like Perplexity reflect source changes within days; models answering from training data can take much longer, sometimes until the next training cycle.
Because the timelines differ, re-check across assistants over the following weeks and track the correction in your AI share of voice. Consistency at the source is what makes the fix stick, everywhere, eventually.