Data Science & ML

What we build

A model that works in a notebook has cleared the easy bar. The hard part is everything after: feeding it reliable data, serving it, knowing when it has quietly stopped being right, and proving it earns its keep. We build the parts that turn a promising experiment into something a business can depend on.

That ranges from analytics and forecasting to recommendation, classification and scoring systems — delivered as a batch job, an API or an embedded library, whichever genuinely fits the problem. We are also happy to tell you when a simple heuristic beats a model, because often it does.

How we approach it

We start from the decision the model is meant to support, not the algorithm. If a result cannot change what someone does, it is not worth productionising. From there we build the unglamorous infrastructure that keeps a model honest.

• Reproducible training — versioned data, code and parameters, so a result can be rebuilt and explained months later.
• Feature pipelines that produce the same inputs in training and in production, so the model is not surprised by the real world.
• Serving that fits the use case — batch scoring where latency does not matter, a real-time service where it does.
• Monitoring for drift and ongoing evaluation, so degradation is caught by a metric, not a complaint.

What you are left with

A model in production that your team can retrain, monitor and reason about — with the evaluation in place to know it is still working. For more on closing that gap, see our note on getting a model out of the notebook and into production.