WHAT WE OFFER
ML
Practical ML — forecasting, scoring, recommendations — built to ship.
Not research papers. Models trained on your data, deployed behind an API, monitored in production, and retrained on a schedule that matches your business.
What we do
Forecasting & demand models
Time-series for inventory, traffic, and revenue planning.
Scoring & ranking
Fraud, risk, lead-scoring, and recommendation systems.
MLOps & deployment
Training pipelines, model registry, drift monitoring, scheduled retrains.
What we know
- Fraud / AML automation reducing manual review ~87%, 10× faster reviews (fintech).
- High-throughput inference paths beyond 2,000 TPS on production backends.
- Python + Go ML services with Prometheus / Grafana / OpenTelemetry observability.
HOW WE WORK
From data to deployed model — monitored and retrained.
We treat ML as software: scoped, shipped, and operated. No notebooks left in production.
PHASE 01
Scope & data
Data audit, target definition, and baseline before any model work.
- 01
Problem framing
Forecasting, scoring, ranking — what is the real business decision?
- 02
Data audit
What you have, what's missing, what's safe to use.
- 03
Baseline & metrics
A simple baseline plus the metric we'll judge models on.
PHASE 02
Train & deploy
Model behind an API, with the operational basics in place.
- 04
Modeling iterations
Disciplined experiments — only the wins go forward.
- 05
Service & API
Inference service, latency budgets, and clear contracts.
- 06
Monitoring
Drift, freshness, and prediction quality watched in production.
PHASE 03
Retrain & improve
Monthly retainer — your model gets better, not stale.
- 07
Scheduled retrains
On a cadence that matches your data — not arbitrary.
- 08
Feature & label review
Quarterly review of what's actually moving the metric.
- 09
Decision reporting
Reports leadership can actually act on.
ML
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