Expert reasoning,
at scale.
Over a billion people can’t access the experts they need. The experts themselves can’t scale. We built the infrastructure to capture expert reasoning, govern it, scale it, and compound it.
ISO 27001:2022Certified
Clinical validation
The problem we’re solving
A billion people.
A handful of experts.
The world’s best judgment sits inside a handful of people. Generic AI trains on what was documented — not on how they reason. It can’t be corrected by the people who know better, and it has no way to keep them in the loop when it’s wrong. UNCAPT is the infrastructure layer between the expert and everyone who needs them. The experts stay at the frontier. We handle everything else.
The category didn’t exist.
It does now.
Five products.
One platform.
Each co-built with a domain expert.
Expert clinical eyes on every case.
vs GPT-4o's 56% · UNCAPT · under review
A support plan built around a person's actual life.
From 2 hours to 15 minutes · University of Newcastle
Support from a system that actually knows them.
Longitudinal support for people with intellectual disability · builds understanding over time
Why domain leaders work with us
Their expertise scales without leaving their hands. They get to keep working at the frontier.
They rarely consult on the same case twice — the system handles scale while they stay at the frontier.
Our co-build model →ELI Platform · Expert-Led Iteration
Uncertainty isn’t a failure mode.
It’s the escalation and training signal.
Every expert correction during normal clinical governance automatically retrains the specific stage verifier that triggered it. No separate annotation pipeline. No extra work.
Expert teaches the system
Expert watches AI reason through a case. Corrects the chain of reasoning — not just the answer. These correction datasets become the stage verifiers.
System halts, expert corrects
Verifier hits something it can't resolve. First, it retries with enriched guidance and additional retrieval. If it still can't resolve, it halts and escalates to the expert. Expert corrects. That correction permanently retrains the verifier.
Every halt sharpens the next
Two phases of proprietary training data accumulate. The system becomes more accurate, more trustworthy, and harder to replicate with every use.
Two phases of proprietary data. One loop.
Proprietary IP · Six components
Six components.
One loop that compounds.
Six components built in-house. Every expert correction makes it more capable. Every case it sees deepens what it knows.
Turns uncertainty into the training signal.
Deep-dive →Research as a versioned, queryable knowledge graph.
Deep-dive →Decision thresholds learned from expert corrections.
Deep-dive →Config, verifiers, and regression tests — each updateable independently. Full audit trail on every output.
Deep-dive →Intake and discovery that sharpens with every cycle of reasoning.
Deep-dive →Runs the OODA pipeline. Manages state between stages and handles the verifier signal at each gate.
Deep-dive →ISO 27001 certified · Azure AU · Full audit trail · Data residency controls
If expertise is the bottleneck,
we should talk.
We work with research groups, hospital networks, and specialist institutions. Every engagement starts with understanding the domain — what knowledge exists, who holds it, what would change if it could scale.