When an AI describes your product wrong, it is repeating something it read. Old pricing on a review site. A two-year-old list article. A Reddit comment about a feature you removed. A homepage that never quite says what you do.
The AI cannot tell those are out of date. The fix: find the sources behind the wrong answer, correct the ones you control, and outweigh the ones you do not.
This matters more than it sounds, because buyers check. In a survey of 550 AI shoppers, 89.9 percent verify AI advice with another source before buying. Among B2B tech buyers, 94 percent fact-check AI research.
And 58.8 percent of visitors referred by an AI land on the homepage. Say the AI quotes “$29 a month, for agencies” and your homepage says “$12 a month, for freelancers”. The buyer sees the mismatch within one click, and trust goes with it.
The three usual reasons
1. A review profile or list has stale facts
G2’s own research found that AI descriptions of a software product closely track the text on its G2 profile. The same goes for Capterra, Product Hunt and the App Store listing. Set up a profile at launch with “coming soon” features and “free during beta”, never update it, and that is what the AI learned.
List articles are worse, because you do not own them. A “best invoicing apps 2024” article that says you cost $29 will say so forever, unless the author updates it.
2. Your homepage does not state the facts
AI pulls most of what it quotes from the first third of a page. One analysis of 815,484 query and page pairs put it at 44.2 percent of citations. If your first screen is a hero image and a slogan, the AI has nothing to quote. It goes to someone else’s description of you.
The cheapest accuracy fix: a first paragraph saying what it is, who it is for, what it costs and what it does not do.
3. The fresh source is wrong and the correct one is old
Models lean toward recent pages. In one study, newer dates moved passages up the rankings in all seven models tested. Ahrefs found that cited content is fresher than average.
So a newer wrong page can beat an older right one. A blogger wrote “Ledgerly is for agencies” last month. Your accurate about page has not changed since 2024. Guess which one wins.
How to find the wrong sources
Ask each assistant the branded questions a buyer asks once they know your name:
- “is [product] any good”
- “[product] reviews”
- “what is [product] and who is it for”
- “how much does [product] cost”
Run each three times, signed out, on ChatGPT, Gemini, Claude, Perplexity and Muse. Note every wrong fact and the source cited next to it.
Usually two or three pages sit behind the errors. That is the fix list.
What works, and what does not
Works:
- Updating every profile you control: review sites, Product Hunt, the App Store, your LinkedIn page, Crunchbase. Same name, same one-line description, same price, everywhere.
- A plain fact sheet page on your site. What it is, who it is for, prices, what is included, what you never do, contact. Short sentences, no adjectives. AI loves it, and so do tired buyers.
- A homepage first paragraph that answers the three questions: what, for whom, how much.
- Emailing the author of a stale list with the correction and one line on why it matters to their readers. Many will update it. Some will add you properly while they are in there.
- Fresh, honest comparison and alternatives pages, so the newest page about you is yours and correct.
Does not work:
- Correcting the AI inside the chat. It is polite about it and forgets by the next conversation.
- Faking a “last updated” date without changing anything. Models do reward recency, and faking it is manipulation. We do not do it.
- Deleting the stale page yourself. You usually cannot, and cached copies linger. Outweighing beats erasing.
What we would do
The free check runs unbranded buyer questions. For an accuracy problem, the audit is the right tool. Its 25 questions include branded checks (“is X any good”, “what is X and who is it for”). The report has an accuracy row: each wrong fact next to the source it came from. $149, credited toward the project within 30 days.
In the project we write three pages for you: your story page, one honest comparison, one alternatives page. Each puts the correct facts up front. We set up accurate review profiles on the two sites AI cites most in your category.
The 25 named lists and sites we pitch include the stale ones, with a correction request as the opening line. Then we re-ask the same branded questions at day 30, 60 and 90. $799.
Example: one assistant described “Ledgerly” (our made-up invoicing app) as “a bookkeeping suite for small agencies, from $29 a month”. The real product is invoicing for freelancers at $12. The sources cited: a 2024 list article and an unclaimed Capterra profile. Two emails and one profile update later, the source side is fixed. Whether and when each assistant picks it up is what the re-checks are for.
We cannot edit an AI’s memory. We can make every page it is likely to read say the same true thing.
Questions
Can I just tell ChatGPT it is wrong?
You can correct it in your own chat, and it will apologise nicely. That changes nothing for the next person. The fix has to happen in the pages the AI reads, not in the conversation.
How fast does a correction show up?
Assistants that search the web (Perplexity, ChatGPT with search, Gemini) can pick up a corrected page within days of a re-crawl. Answers from the model's memory take longer, and we cannot speed them up. We re-check the same branded questions at day 30, 60 and 90 so you can see what moved.
What if a third-party site has it wrong and will not reply?
Then we outnumber it. Three correct pages usually outweigh one stale blog post. A profile on the review site the AI cites most, a fact sheet on your site, a comparison page. We also note which stale sources were cited, so you can try again later.