Use case

AI Hallucination Detection

Identify when AI engines provide inaccurate or outdated information about your brand.

Quick answer
AI engines sometimes generate plausible but incorrect information about brands—wrong pricing, nonexistent features, outdated details, or confused identities. These hallucinations are presented with full confidence and can influence thousands of potential customers before being detected.
Expected outcomes

What you can achieve

90%
of brand hallucinations detected
<72h
average detection time for new inaccuracies
-60%
reduction in hallucinations after remediation
4 types
of inaccuracy categorised and tracked
How it works

AI Hallucination Detection with AEO Platform

1

Build your brand fact sheet

Enter verified information about your brand: products, features, pricing, company details, and key claims. This becomes the ground truth for accuracy checking.

2

Scan AI engines for accuracy

AEO Platform queries AI engines with brand-specific prompts and compares responses against your verified fact sheet, flagging discrepancies.

3

Review hallucination report

Examine flagged inaccuracies categorised by type (outdated, fabricated, confused, exaggerated), severity, and engine. Understand which hallucinations are most damaging.

4

Trace hallucination sources

The platform identifies likely content sources driving each hallucination—outdated pages on your site, incorrect third-party descriptions, or conflicting information across sources.

5

Correct and prevent

Follow targeted recommendations to correct source content, update structured data, and implement consistency checks that reduce future hallucination risk.

The solution

How AEO Platform helps

AEO Platform systematically verifies the accuracy of AI engine claims about your brand. The platform maintains a knowledge base of verified brand facts—features, pricing, descriptions, capabilities—and cross-references this against what AI engines actually say. Any discrepancy is flagged as a potential hallucination, categorised by severity and engine.

The platform distinguishes between types of inaccuracies: outdated information (was true but is no longer), fabricated claims (never true), confusion errors (attributes of a similar brand applied to yours), and exaggeration or understatement of actual capabilities. This categorisation helps you prioritise responses and understand the root cause of each inaccuracy.

Proactive hallucination prevention is built into the platform's recommendations. By identifying the content patterns that lead to hallucinations—inconsistent information across your web properties, ambiguous product descriptions, missing structured data—AEO Platform helps you address the root causes that make hallucinations more likely, rather than just detecting them after the fact.

Example queries

Queries to monitor

How much does [your brand] cost?
What features does [your brand] include?
Does [your brand] offer [specific capability]?
What is [your brand]'s refund policy?
When was [your brand] founded?
FAQ

AI Hallucination Detection FAQ

Relevant engines
Get started

Start with the pages and proof that AI can actually use

Run the free audit to see what blocks AI from citing your site. Use the trial when you need ongoing monitoring, attribution, prompt discovery, and team workflows after the first fixes are live.