Roshanak Kavian
Co-Founder and CEO of Indigo Mars, a Los Angeles marketing agency.
AI hallucination — a model generating confident, plausible-sounding information that's factually wrong — is a real, persistent characteristic of how these systems work, not a bug that gets patched away. When it happens to your own business specifically, the error almost always traces back to one of four causes: stale training data, a wrong source page the AI has learned to trust, confusion with a similarly named company, or an outdated directory listing scraped years ago. The fix is a real, structured workflow, not a complaint form.
Why This Happens: The Four Root Causes
Stale training data means the AI learned something about your business at some point in the past and hasn't updated its understanding since — old hours, a discontinued service, a former address.
A wrong trusted source means the AI is pulling from a specific page it treats as authoritative, and that page happens to be outdated, inaccurate, or simply not yours.
Similarly named company confusion is exactly what it sounds like — the AI conflates your business with an unrelated one that shares part of your name, attributing their information, reviews, or history to you.
An outdated directory listing means an old, unmaintained citation somewhere is still feeding incorrect information into the pool the AI draws from, long after it stopped being accurate.
How to Find Out What AI Is Actually Saying About You
Run the same manual test covered elsewhere in this guide — ask ChatGPT, Perplexity, and Google directly about your business, using the real questions a customer would ask. Pay specific attention to anything factually wrong: an incorrect service, a wrong address, a confused identity with another company. Log what you find, since this becomes the starting point for the actual fix.
The Fix Workflow
Correct the source pages the AI is actually citing. If you can identify which page an AI system is pulling incorrect information from, fixing that page directly is the most reliable long-term correction — more reliable than any single complaint or feedback form.
Publish clear, crawlable corrective content. If an error is persistent, a direct, unambiguous statement addressing it — on your own site, where it can be crawled and cited — gives AI systems a clear, current source to prefer over the stale one.
Use in-product feedback where it's available. Most major AI platforms offer some form of a thumbs-down or feedback mechanism on individual responses. This alone rarely fixes things quickly, but it's a real signal worth using alongside the other steps, not a replacement for them.
Monitor and recheck. Corrections to underlying sources don't propagate to AI answers instantly. Treat this as an ongoing check — rerunning the same manual test periodically — rather than a one-time fix you complete and forget.
A Word on Brand Confusion Specifically
If your business shares a name, or part of one, with an unrelated company, this is worth taking seriously as a distinct, ongoing risk, not a one-time annoyance. The fix here overlaps heavily with the broader entity-consistency work covered throughout this guide — consistent schema, a verified Google Business Profile, and a real base of accurate third-party mentions all help an AI system correctly distinguish your business from an unrelated one sharing part of its name.
Where to Go From Here
This connects directly to the trust and consistency signals covered in Why Schema Markup Matters More Than Ever for AI Search, and the manual testing method covered in How to Actually Track Whether Your Business Shows Up in AI Search.
Found something wrong that AI is saying about your business, and not sure how to fix it? Get in touch and we'll help you work through it.
