You’ve changed a lot in the last few years, AI is still reading about the old you. Here is the playbook to change that.
You ask your favorite AI platform to describe your own company. The first paragraph is fine. Flat, but accurate; roughly what you would have written. The second says you serve small and midsize businesses. You moved upmarket two years ago. It is citing a review profile nobody has touched since 2021, and it states the whole thing with the utmost confidence. You do not know how long it has been saying that. You do not know how many enterprise buyers have read it and moved past you. You moved upmarket two years ago. It is citing a review profile nobody has touched since 2021, and it states the whole thing with the utmost confidence.
You do not know how long it has been saying that. You do not know how many enterprise buyers have read it and moved past you.
Absence is a marketing problem. Error is a revenue problem.
The first wave of AI visibility work asked a simple question: When a buyer asks an assistant who solves this problem, does your name come up at all? That question is still worth asking. But it is being overtaken by a harder one. When your name does come up, is what follows it true?
A missing brand is invisible. A misdescribed brand is worse, because the error arrives with authority and the buyer has no reason to question it. It gets repeated in internal evaluations you will never see.
Why you cannot just ask for a correction
Here is the concept everything else depends on: A language model is not a database with a row you can update. It produces answers from what it was trained on and, increasingly, from what it retrieves at the moment of the question. You cannot correct the answer directly. You can only change what the model reads and then wait for it to read again. That makes every fix indirect and evidence-based. Don’t expect a corrected narrative in days. It could take weeks or longer. This is a game of correcting the source, wherever that source may be.
Five kinds of wrong
The fix depends on the failure, so name it first.
Stale facts. Old pricing, discontinued tiers, a headcount from your last funding announcement, a market you no longer serve.
Identity collisions. A similarly named company in another industry, blended into your description.
Attribution errors. Your integration credited to a competitor or a limitation of theirs recorded as yours. These surface hardest in head-to-head comparisons, which is the moment the stakes are highest and your input is lowest.
Outdated sentiment. A single complaint thread from years ago that still colors every summary.
Invented specifics. Products, integrations or certifications you have never had.
Find the source before you fix anything
Ask the model where it got the claim, then trace it. The culprit is rarely your own website. It is usually a stale directory listing, an unmaintained review profile or one well-ranked article that got a detail wrong and has been quoted ever since. Skip this step and you will spend a quarter rewriting pages that were never the problem.
The correction sequence
- Fix your own pages first. The goal is one consistent answer, not one perfect page.
- Confirm you are not blocking the AI crawlers in robots.txt, which is more common than you think.
- State each fact plainly, near a heading that names the topic.
- Reconcile your own contradictions, often the “about” page against the “careers” page against the footer.
- Add organization schema with sameAs links to your real profiles.
- Fix the third-party sources. This is usually where the error actually lives, and most of it is free.
- Work the citation list from the answer, in order.
- Claim your review site profiles, the most common culprit.
- Update Google Business Profile, LinkedIn and any registry entry.
- Earn independent restatement. Repetition across sources you do not control is what moves an answer.
- Target domains the model already cites for your category.
- Put the claim in language worth quoting.
- Fix your press release boilerplate, the most-copied paragraph you own.
- Recheck and log it. Without a log you cannot tell whether anything worked.
- Same prompts, logged out, on a fixed cadence.
- Record the date, the platform and the sources cited.
What you cannot fix
Some errors live in training data rather than in anything retrievable and they persist until the next model generation, no matter what you publish. The tell is source behavior: if the model cites nothing for the claim, you are waiting rather than working, and knowing the difference saves you a quarter.
Prevention costs less than correction
Keep your details consistent everywhere they appear and put a recurring check on someone’s calendar. There is no support ticket for this. No form to submit, no correction desk, no account rep at the model company who can go fix the record. What you have is a public accuracy problem with no obvious place to report it.
Owning your story used to mean writing it. Today it means maintaining it, everywhere, on a schedule.
Connect with BrandDog at www.branddog.com and take control of your narrative.
About BrandDog
BrandDog is a B2B brand and marketing agency that helps second-stage companies close the gap between how fast their business is growing and how clearly it explains itself to the market. Headquartered in Cleveland, the agency partners with scaling companies to realign on strategy, design, and marketing performance so every initiative drives measurable results. Growth stalls not from a lack of ambition but from brand and marketing that fail to mature alongside the business, so the agency protects clarity, enforces sequence, and builds in the right order across brand, web and marketing. BrandDog exists on a simple premise: when brand maturity matches business maturity, momentum follows. BrandDog was recognized among Northeast Ohio’s Smart 50 companies for innovation and leadership in 2025.
