We started
with a question.

Co-founder focused on product strategy and the applied deployment of expert reasoning systems across high-stakes domains.

Leads commercial strategy and partnerships, bringing domain leaders onto the platform.

Leads go-to-market strategy and revenue operations, turning growth goals into clear execution across commercial and enterprise environments.

Oversees financial operations, corporate governance, and strategic finance.

Experts stay in the loop.

Every system we build is designed to keep experts involved — not to replace them. The value is in the expert's correction, not in the system's autonomy. We build around that.

Uncertainty is information.

When the system doesn't know, it says so — and escalates. We never average over uncertainty or hide it in a confidence score. An honest halt is more valuable than a confident wrong answer.

The knowledge belongs to the field.

We co-build with domain leaders, and the knowledge banks we build together belong to the field — versioned, auditable, updateable. We're not building a black box. We're building infrastructure.

Defensibility compounds.

Every correction the system captures makes it harder to replicate. We're not trying to build the best model — we're building the deepest data asset in each domain, generated by the experts who understand it best.

Healthcare first, not healthcare only.

We chose healthcare because the stakes are highest and the expertise most scarce. The platform is domain-agnostic. Healthcare taught us what it means to build a system where being wrong has consequences.

Every answer must be traceable.

Every output can be traced back to the reasoning that produced it, the knowledge that informed it, and the expert who last corrected that stage. Defensibility without explainability isn't defensibility — it's just opacity with a confidence score.

The training signal should cost nothing extra.

Every expert correction — reviewing a case, correcting a reasoning step, updating a guideline — becomes training data as a by-product of normal governance. No annotation pipeline. No separate process. The system improves because experts are already doing the job.

Knowledge should never be rebuilt from scratch.

Every lab re-contextualises the same literature. Every expert reconstructs the same reasoning. We treat accumulated knowledge as infrastructure — versioned, persistent, owned by the field that built it. The goal is permanence, not repetition.