AI is stating our brand information incorrectly. How do we fix it?

Common situations

• It says a discontinued service is still running
• It quotes a price list from several years ago as if it were current
• The address or phone number is the old one
• It mixes the company up with another one

All of these come up in real audits. And most of them are not something AI invented — they are an accurate citation of old information still sitting somewhere.

Why it happens

The cause is usually one of three.

1. No official document carries the accurate information

If nothing on your site can answer a question about price, AI uses whatever it can find — a blog review, a press release from three years ago. Other people fill the information vacuum.

2. The old information is referenced more readily

A three-year-old press release has been copied across several outlets and carries plenty of links. The page you corrected yesterday has not been cited by anyone yet. On trust signals alone, the old document is stronger.

3. The entities got mixed

Information gets tangled with a similarly named company. When the brand notation differs per channel, or the official channels are not linked to each other, AI cannot draw the boundary.

How to fix it

There is effectively no way to tell AI “that is wrong, fix it”. You change the evidence instead.

Step 1 — record what is wrong and how

Put the question in several forms and write down exactly which engine answers wrongly in which sentence. “It is sometimes wrong” gives you no way to confirm improvement. Recording the question wording itself matters, so the same question can be put again later and compared.

Step 2 — trace the source of the wrong answer

Where the answer carries footnotes, follow the links. The cause is usually visible immediately. Often it is an old page on your own site, and then the fix is fast.

Step 3 — build the official page that will serve as the correction

This is the core of it. For each wrong item, put a page that states that fact clearly on your own site.

• Make the question the heading — “How much does the X service cost?”
• Finish the answer in the first paragraph
• State the publication date and the last modified date
• If the fact changed, write the previous content and when it changed alongside

That last item has an outsized effect. Writing “it was A until 2024 and has been B since March 2026” gives AI a basis for judging what is current even when it meets the old information.

Step 4 — pin the facts with structured data

Text on screen is visible only to people. Stating the same facts once more in JSON-LD makes AI use a settled value instead of an inference.

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "The exact registered name",
  "telephone": "02-0000-0000",
  "email": "hello@example.com",
  "sameAs": [
    "https://blog.naver.com/our-account",
    "https://www.instagram.com/our-account"
  ]
}

Connecting the official channels through sameAs also reduces the entity-mixing problem.

Step 5 — request indexing and track it

Once the new page exists, the search engines have to be told. Naver and Bing accept immediate submission through IndexNow; for Google you request indexing through URL Inspection in Search Console.

After that it is waiting and asking again. Put the questions recorded in step 1 again on a weekly cadence and check whether the answer changes.

How long it takes

Engine type	Speed of reflection
Live-search based (Perplexity and similar)	Days after indexing
Chatbots that also run web search	Weeks
Answers from trained knowledge only	Very slow (tied to model updates)

In other words, fast-correcting and slow-correcting areas coexist. The realistic approach is to confirm the live-search side catching up first while waiting on the rest.

What not to do

• Trying to solve it by deleting the page — only the address disappears while the copied information stays. Correcting the same address is better
• Updating only the date — leaving the content untouched and raising the modified date is not a way to earn trust
• Copying the same content across several pages — duplicates make none of them stronger. Building one properly is better

Summary

• Wrong AI answers are usually an accurate citation of old information, not an invention
• Asking for a direct correction rarely works; changing the source documents is the practical route
• Recording the question wording is what makes improvement confirmable
• Pin the facts with structured data, request indexing, then track weekly

Navirang runs that tracking for you as citation monitoring. If you want to find out which information is currently being answered wrongly, request a free audit.