AEO basics · Oct 26, 2026 · By Gerald De Oleo

How to Fix a Wrong AI Description (It Happened to Me)

The short answer: when AI describes you wrong, publishing more content won’t fix it. The model isn’t missing information — it built the wrong entity for you and is filling the gaps with whatever looked close enough. You fix it by making your identity explicit (structured data), stating plainly what you are and are not, keeping every description of you identical everywhere, and then waiting for the engines to re-crawl.

I do this work for a living, so read the rest with that in mind. But the example below is my own site, and I got it wrong too.

What happened

Last week I asked Gemini about my own company, aeogrowth.co.

It told me — confidently, with sources listed — that aeogrowth.co and aeogrowth.ai are the same project, run by the same company, and split into a consulting arm and a software platform with a share-of-voice dashboard.

I have never heard of aeogrowth.ai. It isn’t mine. There is no dashboard. When I searched for it, I found nothing connecting the two.

So the model didn’t just get a detail wrong. It merged two different businesses into one, invented a product line for me, and explained the split between them in detail.

Why engines do this

An AI engine doesn’t store a page about you. It builds an entity — an internal idea of what “AEO Growth” is — out of everything it can find, and then answers from that.

When the signals are thin or ambiguous, it doesn’t say “I’m not sure.” It fills the gap with the nearest plausible thing. In my case: a similar domain in the same category.

Three conditions make this almost inevitable:

• A young or small footprint. Few third-party mentions means few signals to resolve the ambiguity. Most of what existed about my site, I wrote myself.
• An ambiguous name or acronym. “AEO” is also a clothing retailer, a product name, and a dozen other things. Semrush was matching my blog posts to searches for aeo insights g2 and wondershare uniconverter reviews. If a keyword tool can’t tell what AEO means, a model with less context certainly can’t.
• No explicit self-description in machine-readable form. If you never state what you are, something else will.

This is the fourth layer of answer failure — not “can the crawler read you,” but “did it understand what it read.” I break down all four in the four layers of AI answer failure.

“Isn’t this just a hallucination? Won’t it fix itself?”

No, and that’s the uncomfortable part. A hallucination is random. This is the model reasoning correctly from bad inputs — same domain root, same category, no contradicting signal. It will keep reaching the same conclusion every time, for every buyer who asks, until the inputs change. Nothing expires on its own.

The four fixes

In order of how much they move the needle.

1. Name yourself explicitly in structured data

This is the one most sites skip, and the one that does the most work. Schema is the only place you get to state your identity in a form built to be parsed rather than interpreted.

What that means concretely:

• An Organization entity with your name, URL, description, and a sameAs pointing at your real profiles
• A Person entity for the founder or operator, with worksFor pointing back at the Organization
• Both linked with @id so the relationship is explicit, not inferred
• One entity per thing. Not two.

That last point tripped me up. My site had two business entities — one named “AEO Growth” and another named after me — plus pages pointing at whichever one they felt like. I was asking engines to resolve my identity while handing them two candidates. I merged them into one.

2. Say what you are not

If a model is confusing you with something specific, name the confusion in plain text on a page it will crawl:

AEO Growth (aeogrowth.co) is an independent consultancy run by Gerald De Oleo. It is not affiliated with aeogrowth.ai or any other similarly named company.

One sentence. It reads slightly awkward to a human and removes all ambiguity for a machine. I put it on my About page and in my llms.txt.

3. Check your entity type isn’t lying about you

Here’s a mistake I’d been shipping without noticing. My Organization schema was typed as ["Organization", "ProfessionalService"]. ProfessionalService inherits from LocalBusiness, which expects an address and a phone number — because it describes a business with a physical location and a service area.

I’m an independent consultant who works with clients anywhere. Semrush flagged 29 invalid entities across the site, all for the same missing field. But the broken validation was the smaller problem. The real one: I was telling every engine that reads schema that I’m a neighbourhood business, which quietly contradicts everything my pages say.

Dropping the subtype to plain Organization fixed both.

4. Make every description of you identical

The description on your homepage, your About page, your llms.txt, your LinkedIn, your directory listings and your review profiles should say the same thing in the same words. Models cross-reference. Four different descriptions give them four candidates to blend — which is exactly how companies end up described as something they stopped being two years ago. More on that in Cited by AI, Described as the Wrong Company.

What I actually shipped

The whole fix, start to finish:

Total: under an hour of work. The hard part wasn’t the implementation — it was noticing.

What happens next, honestly

I don’t know yet whether Gemini has stopped merging me with aeogrowth.ai, and I’m not going to pretend otherwise.

Entity corrections are not instant. The engines have to re-crawl, re-process, and in some cases wait for a training refresh. Live-retrieval surfaces — Perplexity, ChatGPT search, Google’s AI answers — pick up changes within days or weeks. Anything answered from training data takes considerably longer, and some of it won’t update until the next model.

So the honest timeline is: check again in two weeks, then monthly. I’ll report back either way.

“If it takes that long, is it even worth doing?”

The delay is the argument for doing it now rather than the argument against doing it. Every week the wrong entity stands is a week of buyers getting the wrong answer about you — and the correction can’t start until the signals change. The companies that fix this early are correcting a small error. The ones that wait are correcting an error that’s been reinforced by a year of citations.

Quick answers

Why does AI describe my company wrong? Because it built an entity for you from incomplete signals and filled the gaps. Common causes: a young site, an ambiguous name, no explicit structured data, or an old description still circulating on third-party pages.

Can I just email the AI company and ask them to fix it? Not practically. There’s no correction desk for entity errors. You change the inputs the model reads, then wait for it to re-process them.

Will more blog posts fix it? No. Volume doesn’t resolve ambiguity — specificity does. One explicit schema entity and one clear disclaimer sentence outperform ten more articles.

How long until it changes? Days to weeks on live-retrieval surfaces. Longer for answers drawn from training data. Re-check the same questions monthly rather than assuming it’s done.

How do I know what the models currently think I am? Ask them directly, in a fresh temporary chat, with neutral phrasing: “What is [company]?” and “Who runs [company]?” Don’t lead the question — a prompt that hints at the right answer will get you one. You can also run a quick check with the positioning check.

My schema is fine but I still don’t appear at all. Then this isn’t your problem yet. Start one layer earlier: most AI crawlers don’t run JavaScript, and a site that looks fine in a browser can return an empty shell to a bot. Two-minute test with the crawler check.

How we work

This is layer two of four, and it comes before content for a reason: publishing into a wrong entity just gives the model more material to misattribute.

With clients, I run the identity questions across every engine first — what they say you are, who they confuse you with, which sources they’re drawing that from. Then we fix the entity: schema, explicit disclaimers, one description everywhere, third-party listings corrected. Only after that do we build pages, because then the pages land on an entity the model has right.

Full scope on the services page.

Want to know what the models currently think your company is? Send me your domain and I’ll ask every engine what you do, who runs it, and who they confuse you with — plus the sources behind each answer. Free, before we talk. Book a call.