We gave AI a sense of uncertainty
Any LLM gains the ability to know when it's wrong — with 91.7% accuracy. No retraining. No external checker. One adapter.
AI can't tell you
when it's lying
LLMs hallucinate 15–20% of the time. They generate with equal confidence whether right or fabricating.
Hallucinated drug interaction
→ Patient harm
Fabricated case citation
→ Sanctions
Wrong regulatory guidance
→ Compliance violation
Confident wrong action
→ Irreversible damage
“The model has no mechanism
to know when it's wrong.”
The fundamental flaw in every LLM
The Solution
We add a nervous
system to any LLM
One proprietary adapter layer. The base model stays completely frozen. We monitor every layer from the inside — not from the outside.
What this is not
retrainingNo.
pre-training data neededNo.
external checkerNo.
multi-samplingNo.
degradation at initZero.
Per-token confidence
Every word gets a calibrated uncertainty score
Conflict detection
Knows when its own layers disagree
Selective gating
Filters irrelevant context automatically
Working memory
Entity tracking beyond the attention window
Generates with equal confidence whether right or fabricating. The user has no mechanism to tell the difference.
The model feels uncertainty while it generates. Every token carries a calibrated confidence score.
Our IP: A proprietary mathematical framework from computational neuroscience — dozens of formally specified mechanisms with convergence proofs.
The market for trustworthy AI
is measured in tens of billions
A normal LLM is a brilliant expert who never says “I don't know.” We fixed that. Any LLM gains the ability to feel uncertainty with >91.7% accuracy.