ML
Machine Learning
Predictive models wired into the decisions they're meant to improve
Overview
Forecasting, anomaly detection, recommendation, and computer vision — built by ML engineers who deploy models into production systems, not notebooks that die in a slide deck.
Industry surveys keep finding that most ML models never reach production. The failure is rarely the math — it's everything around it: training data that doesn't match live data, no pipeline to retrain as the world drifts, and predictions that arrive in a dashboard nobody's workflow actually consults.
We build the whole loop. Feature pipelines fed by your operational systems, models validated against business metrics rather than just AUC scores, deployment into the software where decisions happen, and monitoring that flags drift before it costs money. The model is maybe 20% of the work; we own the other 80% too.
31%
average forecast error reduction across demand planning engagements
$4.2M
annual fraud losses prevented for a single payments client
<50ms
p99 inference latency on real-time scoring systems
Capabilities
What our ml practice covers
Every engagement is scoped from these building blocks — mixed to fit your product, not a package.
Demand forecasting & optimization
Inventory, staffing, and capacity models that turn historical noise into plannable signal — typically 20–35% error reduction versus spreadsheet methods.
Anomaly & fraud detection
Real-time scoring for transactions, sensor streams, and user behavior with false-positive rates tuned to your review team's actual capacity.
Recommendation & personalization
Ranking systems for commerce and content that lift engagement measurably — evaluated by A/B test, never by vibes.
Computer vision
Defect detection, document extraction, and safety monitoring running on cloud GPUs or edge hardware on the factory floor.
MLOps & model serving
Feature stores, training pipelines, versioned deployments, and drift monitoring — the infrastructure that keeps models honest after launch.
Decision science & experimentation
Uplift modeling and experiment design so you know what the model changed, not just what it predicted.
Engagement
How this engagement runs
Four phases with named deliverables — you'll know exactly where the work stands every week.
01
Data & opportunity audit
We profile your data's actual predictive power with quick baseline models before anyone commits to a roadmap.
02
Baseline & benchmark
Simple models first — they set the bar, and sometimes they win. Complexity has to pay for itself.
03
Production pipeline
Feature engineering, training automation, and deployment into the operational system where the prediction gets used.
04
Monitor & retrain
Drift detection, scheduled retraining, and quarterly value reviews tying model performance to business outcomes.
FAQ
ML questions, answered plainly
Ready to talk ml?
Bring the problem — a technical lead will sketch the approach, the team shape, and an honest budget range on the first call.