We build AI systems into the workflows you already have, measure what they are actually worth, and hand them over so your team runs them.
In cooperation with SAIT · University of Waterloo · University of Toronto
They fail because nobody can say whether the output is right. A model returns an answer in seconds. The real question is whether someone in operations can check it, stand behind it, and override it when it is wrong.
That is where we work: in processes where mistakes cost money and someone has to answer for them.
Much of what makes frontier models good happens before training: filtering, deduplicating, scoring, weighting. Point a finished model at unchecked operational data and you get fast answers nobody can stand behind.
So every engagement starts with the data actually moving through the process. Not with the model.
We sit with the people doing the work and map the real process along with the data running through it, not the documented version. Every step, every exception, every handoff, every source. You get an operating map plus an honest read on which data holds up and which does not.
We build a test set of 20 real cases from your business, with correct outcomes labelled by hand. The system runs against it. You get a pass rate, a list of failure types with counts, written escalation rules, and cost per run. From here the conversation stops being an opinion.
We build on what you already have. No migration. Sandbox first, human sign-off on every action, autonomy widened step by step. Everything logged, everything reversible.
Canadian operators ask this early, so we answer it early. Nothing needs to leave the country for us to do this work, and we will tell you plainly if a particular tool would require it.
Operating map, one workflow selected, test set designed, a straight recommendation on whether to proceed.
Credited in full against the build.
Build, measurement, sandbox, staged rollout, handover to your team.
Test set re-run, drift reported, failure types and escalation rules kept current.
When an AI system moves into a workflow, people end up supervising something they did not build. We measure where your team stands, close the gap by role, and measure again.
Part of every build, and available on its own.
Client names on these projects stay private because sensitive financial data was part of the work.
The Starter Sprint ends with an operating map and an honest read on whether automation pays here. Including when the answer is no.