Artificial Intelligence
Design and deliver AI systems — document intelligence, LLM applications and decision support — that are secure and governed.
Machine Learning
Machine learning engineered for production, not presentations
Production machine learning for risk, fraud and forecasting — from feature engineering to MLOps and monitoring.
The problem
Why this matters
A model is a small part of a production ML system. Reliable feature pipelines, reproducible training, controlled deployment and monitoring determine whether predictions remain accurate after launch.
Common challenges
- Training–serving skew and data leakage
- Manual, unrepeatable model deployment
- Silent model degradation in production
- Explainability requirements for regulated decisions
Our approach
How we work
-
Frame
Define the target, decision and success metrics with the business.
-
Engineer features
Build point-in-time-correct features shared by training and serving.
-
Develop & validate
Train, validate and document models reproducibly.
-
Deploy & monitor
Automate deployment with shadow testing and drift monitoring.
Capabilities
What our machine learning work covers
- 01
Risk & fraud models
Supervised models for credit, fraud and claims.
- 02
Feature stores
Consistent online and offline feature computation.
- 03
MLOps pipelines
CI/CD for models with registry, approval and rollback.
- 04
Explainability
Local and global explanations, including SHAP-based reason codes.
- 05
Model monitoring
Data drift, performance and fairness monitoring.
- 06
Forecasting
Demand, cash-flow and volume forecasting.
Engagement
Deliverables and benefits
What you receive
- Production ML pipeline
- Model documentation and validation evidence
- Monitoring dashboards and alerts
- MLOps runbooks
What it changes
- Reproducible, auditable models
- Faster, safer model updates
- Early warning when models degrade
Standards & technology
FAQ
Frequently asked questions
Do you prefer deep learning or classical models?
We choose the simplest model that meets the requirement. For tabular risk data, gradient-boosted trees are often the best balance of accuracy and explainability.
Discuss your machine learning requirements
Let’s discuss it. Tell us what you are working on and an engineer — not a sales script — will respond.