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Use case

Fix the things AI gets wrong about you

When ChatGPT invents a feature, misstates your pricing or puts you in the wrong category, every buyer who asks inherits that error. Find each hallucination, trace it to its source, and correct the record.

Catch every wrong claimTrace it to the sourceRe-check on every scan
aurvikon · live scan
Illustrative
Buyer prompt
“Best [your category] for [buyer need]?”
AI answer — top picks
1[Your brand]cited
2Competitor A
3Competitor B
Sources:docs.sitereview.ionews.co

A confident wrong answer costs you the deal before you speak

Buyers can't tell a hallucination from a fact. If the model says you don't integrate with a tool you've supported for years, that's a lost opportunity you never even saw arrive.

Invented facts
Features, integrations or limits you never claimed, stated as truth to every prospect.
Wrong category or price
Misplacement that lines you up against the wrong competitors, or scares off buyers with bad numbers.
It keeps coming back
Fix it once and a hallucination can reappear as sources or models refresh.

Catch it, source it, correct it — then keep watch

Detect the errors
Aurvikon captures how each engine describes you and surfaces the claims that are wrong.
Trace the source
See which pages and sources likely produced the error, so you fix the cause, not just the symptom.
Correct the record
Generated schema, llms.txt and clear canonical facts give the model the right answer to retrieve.
Guard against relapse
Drift monitoring re-checks every scan, so a returning hallucination is caught early.
How it works
01
Find the hallucinations
Scan every engine and flag the descriptions that don't match reality.
02
Fix the inputs
Correct the sources, publish clear facts, and ship the generated schema.
03
Verify and monitor
Re-scan to confirm the fix, then keep watch for any relapse.
What's inside
Per-engine error detection
Likely-source tracing
Schema + llms.txt correction
Canonical fact surfaces
Drift monitoring on every scan

Questions, answered honestly

Can you make ChatGPT stop saying something false?
Not by decree — no one can. We identify the wrong claim, trace it to the sources the model relies on, help you correct those, and then measure whether the answer improves. That's the honest, effective route.
How do you know what's wrong?
You do — you know your product. Aurvikon surfaces each engine's claims clearly so you can flag what's inaccurate, then focuses the fix on the sources behind it.
Will it stay fixed?
Often, once the underlying sources are corrected. Because hallucinations can return, drift monitoring keeps checking on every scan.
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See what AI gets wrong about you

Run a free brand check and surface every inaccurate claim, per engine.