Applied machine learning

Models in service of decisions — classification, extraction, search, detection. We focus on the full loop, not just the model.

What it covers

Problem framing: the decision the model informs and the cost of being wrong
Feature pipelines and training data that can be regenerated, not rediscovered
Classical ML, a fine-tuned small model, or an LLM — whichever the evidence supports
Model serving and deployment with versioning, rollback, and shadow traffic, for batch and real-time use
Monitoring for drift, quality, and cost after launch

How an engagement runs

We start from a decision that matters and a baseline that is already in place, however crude. We build the thinnest model that beats it on real data, ship it behind a stable interface, and instrument it so the team can see when it stops working.

What you get

A deployed model with reproducible training, an evaluation report in plain language, monitoring, and a handover your team can maintain without us.

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