Service / 08
Data
Science
Machine learning that ships — from notebook to production endpoint.
ML · Modeling · Prediction
A/B
Tested in prod
MLOps
From day one
100%
Reproducible
Overview
We scope the problem, pick the right modeling approach, and ship models that hold up under real traffic. Churn, forecasting, recommendation, scoring — built to be measured, monitored, and improved.
What you get
Deliverables.
Problem framing & success metric
Feature engineering pipeline
Trained & evaluated model
Production API or batch job
Monitoring & drift alerts
Retraining playbook
How we work
Process.
- / 01
Frame
Translate the business question into a measurable ML target.
- / 02
Prototype
Baselines first, then iterate toward the best honest model.
- / 03
Ship
Wrap the model as an API or batch job your team can call.
- / 04
Monitor
Track drift, accuracy, and value — retrain on a schedule.
FAQ
Things people
always ask.
Do we need a lot of data?
Not always. Many high-value models work with thousands — not millions — of rows.
Do you do generative AI / LLM work?
Yes. RAG pipelines, fine-tuning, and LLM-powered features are part of the practice.
Ready to
build it?
Tell us what you have in mind. We'll come back within 24 hours with a clear-eyed take and next steps.
Next service