Audit where AI confidence
exceeds evidence.
Not fact-checking. Not binary verification.
A risk-aware analysis of confidence versus evidence.
Atomic Claims
Prevents rhetorical masking of weak facts by isolating falsifiable units.
Structured Evidence
Blocks hallucinations backed only by model consensus using authoritative knowledge graphs.
Epistemic Risk
Quantifies overconfidence instead of hiding it. Rewards calibrated uncertainty.
Why hallucinations
aren't binary
Modern AI failures are rarely outright falsehoods. They are overconfident claims weakly grounded in evidence.
This system is designed to surface that risk — explicitly. By decomposing text into discrete nodes of inquiry, we move past "True/False" toward "Calibrated/Uncalibrated."
How the System Audits Claims
A slower, guided preview of the epistemic analysis process.
Observe how discrete claims are isolated, cross-referenced, and scored for risk.
Designed for Strict Contexts
Where This System Can Fail
1. Outdated Structured Data
If knowledge references (e.g. SEC filings) are stale, the engine may flag recent valid claims as unsupported. (Disclosure: Latency gap ~24h)
2. Ambiguous Predicates
Language with high semantic drift (e.g. "revolutionary") cannot be rigorously falsified.
3. Registry Gaps
Claims referencing private datasets or non-public events are invisible to the verification layer.
4. Over-Compression
Complex multi-part claims may be atomicized incorrectly, losing context.
System Constraints & Refusals
This is not a truth oracle.
It does not declare facts "true" or "false."
It exposes epistemic gaps — where confidence exceeds evidence.
A tool for human experts to audit the recursive overconfidence of large language models.