The principles behind every system we ship.
Written down so you can hold us to them.
Start with the decision, not the model
The useful question is which human decision you want made faster, cheaper, or better. The model, the vendor, and the architecture are details that follow from the answer. We'd rather spend the first week on the decision than the last month regretting the model.
Measure before you trust
Every model ships with an evaluation set built from your real cases and a number that says how often it's right. If we can't measure it, it doesn't go to production. If the number isn't good enough, we say so.
People stay in the loop
Agents act within limits you set. Consequential actions wait for a person. Every step is logged so you can see what happened and why, and change the limits when you've seen enough.
Private by default
Your data trains nothing you don't own. Deployments are designed around your privacy obligations, with Canadian data residency, private cloud, or on-premise options when the data can't leave.
Prefer boring where boring works
A rules engine, a well-indexed search, or a plain regression beats a language model for a lot of problems. Using the simplest thing that works is a feature. We'll tell you when that's the case.
Keep the exits open
Model providers change prices and retire models. We build so that swapping a provider, a cloud, or a data source is a configuration change, not a rewrite. You should be able to leave us, too, which is why everything ships documented.
Working software, every week
Small teams, direct access to the engineers, and a demo of real progress each week. If the direction is wrong, you find out in days.
How a project runs.
Assess
Two to three weeks mapping your operations, data, and decisions. A written plan with ranked opportunities and a scoped first project.
Prove
The first project, on real data, measured against a baseline you agree before we start.
Build
Production hardening: evaluations, security review, monitoring, documentation, training.
Operate
Hand-over to your team, or managed operation by ours, with the same engineers available either way.
Tell us what you're trying to do.
A thirty-minute conversation with an engineer, not a salesperson. We'll say plainly whether AI is the right tool for it, and what it would take.