Custom Machine Learning Systems

We build production grade machine learning systems for regulated industries, automating high-stakes decisions like risk scoring, pricing optimisation, and complex time series forecasting. Designed for trust, transparency, and ongoing regulatory compliance

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What We Deliver

Production-ready machine learning systems designed for environments where model decisions require stakeholder trust and regulatory transparency. Each implementation includes model development, explainability, and monitoring systems that track both technical and business metrics.

Core Capabilities

Models generate decision explanations in business terms alongside predictions, with automated reporting that maps model factors to business outcomes. Monitoring dashboards connect model performance metrics to operational KPIs. Documentation packages translate technical model improvements into formats suitable for executive and board review.

Technical Approach

Systems are built with versioned data pipelines, automated retraining workflows, and drift detection that flags when model assumptions no longer align with current data patterns. Explainability systems provide feature importance rankings, counterfactual explanations, and confidence intervals for individual predictions.

Usecases

Credit risk scorecards, risk-based pricing, demand forecasting, pricing optimisation, customer lifetime value prediction. Most suitable for use cases where prediction reliability and decision audibility are regulatory or business requirements.