Who hires us for this
Teams with a data problem who suspect ML might be the answer — and want an honest second opinion before they commit a quarter to building it.
Data science & machine learning
Analytics, modelling, and ML systems that survive contact with production — not just notebooks.
How we approach it
We're language-honest. Not every problem needs ML. We will tell you when a SQL query, a heuristic, or a small model on a CPU is the right answer — and only build the heavy machinery if that's what the problem really demands.
What you get
Either a production ML pipeline (training, serving, monitoring, rollback) — or a written second-opinion brief explaining why you don't need ML and what would actually solve the underlying problem.
Detail
Most ML work fails in the gap between a notebook that works and a system that runs at 3am. We bridge that gap. Our ML engagements deliver the model AND the surrounding infrastructure: data pipelines you can reproduce, model versioning you can roll back, monitoring that flags drift before users do, and serving endpoints that are boring to operate.
We're language-honest: not every problem needs ML. We'll tell you when a heuristic, a SQL query, or a small model on a CPU is the right answer. We won't try to sell you a transformer for what regex would solve.
Technologies we use
Want this expertise on your project?
Tell us what you are building. We will say in one email if we are the right partner.
Let's talk