Production ML from proof-of-concept to deployment — models that live in your product.
Most ML projects stall between a notebook and production. We handle the whole path — frame the problem in business terms, build and validate the model on your data, and deploy it with monitoring, retraining, and a plan for when it drifts.
It’s the right fit for teams with a defined ML use case ready to productionize, a POC that needs to become a real service, or an ambitious analytics practice ready for its first ML feature.
We ship models to production and monitor them — the notebook-only stage is over.
Every engagement ships against outcomes we agree up front.
Trained and validated on your data.
Training, deployment, and retraining in code.
Behind APIs, in batch jobs, or embedded — whatever fits.
Drift detection and a retraining plan.
Into your product or workflows — not a side dashboard.
A baseline, a target, and honest reporting.
A real sequence — each step earns the next.
Business question turned into a measurable ML problem.
Features, model, and evaluation.
Pipeline, service, and monitoring in production.
Drift, performance, and retraining under review.
Concrete artifacts you take away from the engagement.
No models die in notebooks on our watch.
Baselines, targets, and no cherry-picked metrics.
Reproducible builds and deployable pipelines.
Every project runs with a senior ML lead.
It depends on the task — sometimes less than teams expect. We scope after a quick data review.
Both. We’ll be explicit about which stage we’re shipping into.
We set a baseline first, then commit to a lift. Anyone promising a fixed number without your data is guessing.
Behind APIs, batch jobs, or embedded in your product — whatever fits.
A well-scoped POC-to-production run is typically 8–12 weeks.
Tell us what you’re building — we’ll reply within one business day.