Machine learning & data science
Models where a model is genuinely the right answer.
The problem
What usually brings people here.
Sometimes the answer really is a model — a forecast, a ranking, a risk score — and sometimes it is a query someone has not written yet. Starting with the model is how teams end up maintaining a pipeline that a case statement would have handled.
What we do
How the work actually runs.
We check the baseline first: what does the simple rule get you, and is the gap worth a model's lifetime cost. If it is, the work runs against a held-out set from the beginning, with the metric agreed with the business before training starts. You get the model, the evaluation, and an honest account of where it is weak.
How this runs
Design, develop, refine.
Design
Two to four weeks. We scope with the engineers who will build it, so the estimate comes from the people held to it.
Develop
Two-week increments, demoed to you, shipped behind flags. You see working software in week three, not a status deck.
Refine
The squad stays. Lead time comes down, the backlog gets shorter, and the same people know why every decision was made.
Proof lives on the work page.
We publish numbers per engagement, cleared by that client, rather than per service line. What is written up there is what we are allowed to show.
Also in AI Engineering
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Tell us what you are trying to ship.
You will get a reply from an engineer, not a form handler. If we are not the right firm for it, we will say so and tell you who is.