Service 03 / 08
Custom ML & Data Engineering.
For enterprises whose problems don't fit off-the-shelf models — and whose data pipelines aren't ready for the ones that do.
The practice
What this engagement looks like.
Off-the-shelf models know the internet; they don't know your loss history, your patient population, or your SKU velocity. The gap between generic and domain-trained is routinely 15–30 points of performance.
We take problems end to end — data audit, feature engineering, training, validation, deployment — inside your cloud and your security review, handing over documented, reproducible pipelines rather than notebooks.
What you get
Deliverables, not decks.
01
Production model & serving infrastructure
02
Feature store & data pipelines
03
Explainability layer & model cards
04
MLOps handover with retraining playbooks