When an AI states something wrong about your brand, you don't fix it by arguing in the chat. You fix it by changing what the engine pulls from: your own pages and the broader web it trusts. Sooner or later it will get something wrong. Last year's pricing, a product you discontinued, a feature that's actually a competitor's, or a flatly negative framing, and because the answer reads confidently, the buyer believes it. Here's the playbook for correcting it across the engines.
1. Diagnose where the error lives
The fix depends on the source, so check across engines first (how to measure):
- Wrong on every engine → it's likely baked into training data or your entity: the web's general "knowledge" of you is outdated. Slow to fix, but fixable.
- Wrong on one engine, right on others → it's a retrieval / source problem on that engine. It's reading a bad or stale source. Faster to fix.
2. Fix your own source of truth
Make the correct facts reachable and unambiguous on pages you control. Accurate pricing, current product lineup, clear positioning. In clean, structured HTML so engines can ground answers in them. If the right answer isn't easy to retrieve from you, the engine fills the gap by guessing or by trusting someone else.
3. Correct the corroborating web
Engines lean heavily on third-party sources. If retailers, review sites, directories, or your Wikipedia/Wikidata entry carry stale facts, the model will trust the consensus over your lone correction. Update the places that describe you, so the web tells one consistent, current story.
4. Refresh, and re-publish
Stale pages lose out to fresh ones in live retrieval (the citation cliff). A meaningful update. Corrected facts, a new "last updated". Signals currency and helps the corrected version win.
5. For training-baked errors, play the long game
You can't edit a model's weights. What you can do is make the correct information so consistent and well-corroborated across the web that the next training cycle learns the right version, while live retrieval carries the correction in the meantime.
6. Watch it propagate
A correction isn't done when you publish it. It's done when the engines repeat it. Track the specific wrong claim across ChatGPT, Gemini, Claude, and Google's AI surfaces until it flips.
Most "AI is wrong about us" problems are really "the correct answer isn't reachable or corroborated enough for the engine to prefer it." Fix that, and the answer fixes itself.
That diagnose-fix-verify loop. Across every engine, tracked over time. Is exactly what Buffy Intel is built to run.