Predictive Models That Work in Production
Most ML projects fail at deployment. We build models with production-readiness in mind from day one — with the MLOps infrastructure, monitoring, and governance required to keep them reliable long-term.
From experiment to enterprise-grade model
A Jupyter notebook is not a product. We take ML projects through the full lifecycle — rigorous feature engineering, model selection, evaluation against business metrics, and deployment into robust API services or batch pipelines.
We build explainability in from the start, so your compliance and audit teams can validate model decisions — essential in regulated sectors including finance, healthcare, and government.
Our ML tech stack
ML capabilities we deliver
Production ML across supervised, unsupervised, and reinforcement learning — applied to real business problems.
Revenue & Demand Forecasting
Predict future demand, sales, and resource needs with time-series models trained on your historical data — reducing overstock, underserving, and planning errors.
Fraud & Anomaly Detection
Real-time ML models that flag unusual patterns in transactions, user behaviour, or operational data — stopping fraud and errors before they escalate.
Customer Churn Prediction
Identify at-risk customers weeks before they leave. Prioritise retention actions where they have the highest impact with propensity scoring models.
Risk Scoring & Credit Models
Regulatory-ready risk models with full explainability reports — satisfying audit requirements while improving accuracy over legacy rule-based systems.
Predictive Maintenance
Sensor and IoT data analysis that predicts equipment failures before they happen — reducing downtime and maintenance costs in logistics and manufacturing.
NLP & Text Classification
Automated categorisation, sentiment analysis, and entity extraction from unstructured text — emails, support tickets, contracts, social media.
MLOps: keeping models accurate over time
Models degrade as data patterns shift. We build the monitoring and retraining infrastructure to keep your ML investment performing.
Drift Detection
Automated monitoring for data drift and model performance degradation — with alerts when models need retraining.
CI/CD for ML
Automated pipelines for model validation, testing, and deployment using Azure ML and GitHub Actions.
Model Registry & Versioning
Full audit trail of model versions, training runs, and deployments — meeting governance and compliance requirements.
Ready to put your data to work?
Tell us your prediction problem and we'll tell you what's feasible, what data you need, and what ROI looks like.
Talk to a Data Scientist