Predictive Analytics
What we do
We build ML pipelines that deliver forecasts stakeholders trust and act on. Our approach emphasises model interpretability, probability calibration, and operational reliability across the full lifecycle from training through production monitoring. Our team has built predictive systems for sports outcome modelling, demand forecasting in logistics, churn prediction in SaaS, credit risk scoring in financial services, and clinical outcome prediction.
Demand & Revenue Forecasting
Time-series models using Prophet, ARIMA, DeepAR, or Temporal Fusion Transformers for inventory planning, revenue projection, and capacity management. We handle seasonality, holiday effects, promotional impacts, and external regressors to produce forecasts that operations teams can plan around with confidence.
Churn & Retention Prediction
Customer lifecycle models that identify at-risk accounts weeks or months before they disengage. We combine behavioural signals, usage patterns, and engagement metrics to produce risk scores with actionable thresholds, enabling your customer success team to intervene precisely where it matters most.
Risk Scoring & Credit Assessment
Classification models for credit risk, fraud detection, and compliance screening built for regulated industries. We implement model governance frameworks, audit trails, adverse action explanations, and bias monitoring to satisfy regulatory requirements while maintaining predictive performance.
Anomaly Detection & Alerting
Real-time and batch anomaly detection systems that surface unusual patterns in operational data, financial transactions, or infrastructure metrics. We use isolation forests, autoencoders, and statistical process control methods calibrated to minimise false positives while catching genuine anomalies.
How we work together
Data Assessment & Feature Engineering
We audit your data sources for quality, completeness, temporal consistency, and predictive signal before committing to a modelling approach. We identify and engineer features using domain knowledge, assess data leakage risks, and establish train-validation-test splits that reflect real-world deployment conditions. Data preparation and feature engineering consistently determine prediction quality more than model architecture.
Model Development & Validation
Iterative model development using scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow depending on the problem characteristics. We benchmark multiple approaches against business-relevant metrics including cost-weighted accuracy, calibration plots, and decision-threshold analysis alongside standard measures like RMSE and AUC. Every model undergoes bias and fairness evaluation before deployment.
Production Deployment
Model serving infrastructure using MLflow, BentoML, or custom FastAPI services with monitoring for prediction drift, feature drift, and performance degradation. We implement automated retraining pipelines triggered by performance thresholds or scheduled intervals, with A/B testing frameworks that validate new models against production baselines before full rollout.
Stakeholder Integration
Dashboards built in Streamlit, Retool, or embedded BI tools that put predictions directly into the hands of the people who need them. We include confidence intervals, feature importance explanations via SHAP or LIME, and alerting thresholds so decision-makers understand what the model predicts, why, and how certain it is.