Agents enterprises
can trust in production.
The evaluation, training and control layer that makes AI agents safe for regulated, high-stakes work — where mistakes are not allowed.
The black box.
The costliest errors happen in regulated, high-stakes work — exactly where agents still can't go. They hallucinate, ignore instructions, and the failure is untraceable. If you can't see it, you can't fix it, and you can't trust it.
Train the errors out — measured, not promised.
A data protocol makes every reasoning step and action visible — turning an opaque agent into something measurable, traceable, and trainable.
A live agent, shipped in weeks.
Wellbeing-risk ECG agent, built on Forge and shipped inside a client's product.
A funnel converting into revenue.
£1.5M — SEIS/EIS, two tranches.
Defended on four fronts.
The market is converging on the same primitives — evals, traces, observability. We own the layer that closes them into one loop.
Makes every reasoning step and action visible — the thing that turns a black box into a trainable system. Nobody else has it.
One design spans training data, held-out verification, and live monitoring — generating tasks, ground truth and traps from client data.
Strategies mapped to task families. Model- and runtime-agnostic: the model stays replaceable, the trained configuration is the asset.
Every public case and private contract grows a proprietary corpus of agent failure modes. Benchmarks drive signal; contracts compound data.