MLOps & evaluation
The harness that tells you a change made things better.
The problem
What usually brings people here.
A model in production that nobody is measuring is a liability with a cron job. Drift is silent, the retraining story is a notebook on someone's laptop, and the only signal that something has broken is a customer complaint.
What we do
How the work actually runs.
Training, evaluation and deployment move into the same pipeline as the rest of your code, with versioned data, reproducible runs and a harness that tells you whether a change made things better. Monitoring covers input drift and output quality, not just uptime. You get a rollback path for models, same as for code.
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
Related services.
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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.