How to Fix Outdated or Incorrect Brand Information Across the Web

A prospect asks ChatGPT about your pricing and gets a number you changed eight months ago. Someone asks Perplexity who your CEO is and gets the person who left last year. Neither is malicious. The model just learned the old fact from a source that was accurate when it was published and has never been told the fact changed.

This is one of the more frustrating problems in GEO because it’s not about earning a citation, it’s about correcting one that already exists and is actively working against you.

Why AI tools keep repeating old information

LLMs don’t check facts against a live database. They generate answers based on patterns learned from training data, sometimes supplemented by real-time retrieval, but even retrieval-augmented answers pull from indexed pages that may not reflect what changed last month.

Once a fact gets written down in a place a model trusts, a review site, a news article, a directory listing, a Wikipedia-style reference, it becomes part of the record the model draws from. If nothing newer and more authoritative displaces it, the old version keeps surfacing. This is different from a normal SEO problem, where updating your own page usually fixes what shows up in search. With LLMs, the outdated version might live entirely off your site, in places you don’t control and can’t directly edit.

Where outdated information tends to live

Before you can fix anything, you need to know where the wrong version is actually coming from. A few categories account for most of it:

Review and directory sites. G2, Capterra, Crunchbase, and similar platforms often carry old pricing tiers, discontinued features, or former leadership names in fields nobody thinks to revisit after the initial listing.

News and press coverage. An article from a funding round or product launch two years ago still describes your company the way it was then. The article isn’t wrong, it was accurate at publication, but a model treating it as current creates the problem.

Wikipedia and Wikidata. If your company has an entry, it’s a heavily weighted source for LLMs. An out-of-date infobox field, an old employee count, or a stale product description here has outsized influence.

Your own outdated pages. Old blog posts, cached comparison pages, or an about page that still lists a past product line. These are the easiest to fix and the first place to check.

Third-party comparison and “best of” content. Competitor comparison pages and roundup articles written by other sites often describe your product based on whatever was true when the piece was researched.

How to find what’s actually wrong

Start by asking the major AI tools direct questions about your brand: pricing, leadership, product lineup, headquarters location, key features. Do this across ChatGPT, Perplexity, Claude, and Google’s AI features, since each pulls from different sources and surfaces different errors.

When you get bad output, ask a follow-up: where did that come from, or what makes you say that. Some tools will name a source or describe the kind of source. That’s your starting point for tracking down the specific page carrying the outdated fact.

Cross-reference what you find against a manual search for the same claim. If an old figure or fact keeps showing up in review sites, press archives, or directory listings, that’s very likely where the model picked it up.

Fixing it at the source

Once you know where an incorrect fact lives, the fix depends on who controls the page.

1. Pages you control. Update them immediately. This includes your own site, your G2 or Capterra profile (most platforms let vendors claim and edit these), your Crunchbase entry, and your social profiles. This step alone won’t fix an LLM’s answer overnight, but it stops the wrong version from being the only current source available.
2. Pages a partner controls. Reach out to directories, integration partners, or affiliate sites that list your product and ask for the correction. Most will update a factual error quickly if you flag it directly rather than filing a generic support ticket.
3. Press and editorial coverage. You usually can’t get an old article rewritten, and trying to isn’t worth the effort in most cases. Instead, reach out about a follow-up piece or an updated mention that reflects the current facts, so a newer, corroborating source exists alongside the old one.
4. Wikipedia and Wikidata. Edits here need to follow the platform’s neutrality and sourcing rules. A direct edit from a company account is often reverted. The reliable path is providing well-sourced, verifiable information to someone with edit history on the page, or through the platform’s own correction channels.

When you can’t get the old version removed

Some outdated information simply won’t get corrected or removed on any reasonable timeline. In that case, the practical move is to outcompete it rather than chase a takedown.

Publish a clear, current, well-structured page on your own site that states the correct fact plainly and includes a date. A dated correction signals recency to both crawlers and any model doing retrieval at query time. Pair it with a fresh mention or two elsewhere, a comparison page, a directory update, an interview, so there’s more than one current source available. Models weigh corroboration, so giving them multiple recent, consistent sources helps displace a single old one even when that old one is still technically live.

Source type	Best fix	Typical timeline
Your own site	Edit directly	Immediate
Review or directory listing	Claim profile and update, or contact support	Days to a few weeks
Partner or integration page	Direct outreach to update	Days to a few weeks
News or press archive	Rarely editable; pursue a follow-up piece instead	Weeks to months
Wikipedia or Wikidata	Submit sourced correction through proper channels	Weeks, and not guaranteed

Make this a recurring check, not a one-time fix

Facts about your company change more often than most teams update the pages that describe them. Pricing shifts, features ship and sunset, leadership changes. Treat a brand-accuracy check as a recurring part of your content strategy, not a one-off cleanup after you notice a bad answer. Rerunning your core brand prompts against the major AI tools every quarter catches drift before a prospect catches it first.

If you’re finding AI tools consistently getting your brand wrong and don’t have a system for tracking it, get in touch. We’ll show you exactly what’s being said and where it’s coming from.