Led by practitioners with extensive experience developing, evaluating and assuring AI for safety-critical and regulated environments — where an answer has to arrive with its evidence and survive review.

The same discipline runs through the platform itself. Every answer arrives with the evidence behind it, access stays scoped to what the work actually requires, and your data stays inside your boundary — so a reviewer, or a regulator, can follow how an answer was reached instead of taking anyone's word for it.

A scoping call to pick the one question worth answering. A pilot measured in weeks, on your infrastructure, with your team watching it get built. Then a decision — continue, extend to a second mission, or stop. You keep what was built either way.

The mission configuration, the connected sources and the answers it produces are yours. Nothing is locked to infrastructure of ours, and nothing stops working if an engagement ends.

Every claim carries its source, its timestamp, and its uncertainty. If an answer can't be traced back to a record, it doesn't ship — in this work, an explanation a reviewer can't follow isn't an explanation.

High-consequence, regulated decisions, where being wrong is expensive and the model said so will not survive review: aviation safety and air-traffic modernization, federal program delivery, intellectual property, and clinical decision support.

Trellis runs in your environment, on your data — open-source-first and cloud-portable, with nothing tied to infrastructure of ours. The people who know the mission keep control of it, rather than waiting in a vendor's engineering queue.

Five missions running on real data from one codebase — aviation, patents, weather, personal health records, and bicycle supply-chain risk. We are taking on a small number of design partners.