We design and deploy bespoke machine learning models that learn from your data to forecast demand, detect anomalies, recommend actions, and classify inputs — with full MLOps pipelines so models stay accurate as your data evolves.
PyTorch · TensorFlow · XGBoost · MLOps Pipeline · Real-time Inference · Monitoring
Forecasting models trained on your specific data outperform off-the-shelf analytics tools — they capture your seasonality, trends, and business-specific patterns. We build demand forecasting, churn prediction, revenue projection, and risk scoring models tailored precisely to your domain.
We build time-series forecasting models using LSTM, Prophet, and gradient boosting methods that account for seasonality, promotions, external signals (weather, events), and lag features specific to your business. Models are trained on your historical data and evaluated against held-out periods to validate real-world accuracy before deployment.
Predicting which customers are likely to churn or which accounts carry elevated risk lets you act before the outcome occurs. We build binary and multi-class classification models using gradient boosting or neural approaches, with calibrated probability outputs so your teams can prioritise interventions by risk score — not just flag a binary yes/no.
Predictions that can't be explained aren't trusted by the teams that need to act on them. We integrate SHAP, LIME, and feature importance analysis to make every model decision interpretable — showing which factors drove a specific prediction and giving your stakeholders confidence to rely on model outputs in real decisions.
Whether it's fraud in financial transactions, defects on a production line, or unusual network traffic, anomaly detection at scale requires models that understand what "normal" looks like in your specific context — and can flag deviations in milliseconds.
We train isolation forest, autoencoder, and graph neural network models on your transaction and event data to detect fraudulent patterns in real time. Models are designed to minimise false positives — which damage legitimate customers — while maintaining high recall on true fraud cases. Continuous retraining handles evolving fraud patterns automatically.
Computer vision and time-series models monitoring equipment telemetry, sensor readings, and production imagery to detect early signs of failure or quality defects — often days before a human inspector would catch them. Integration with SCADA and MES systems means alerts trigger automatically in your existing operations workflows.
Personalisation at scale drives measurable revenue. We build collaborative filtering, content-based, and hybrid recommendation systems that improve as user interaction data accumulates — and that can handle cold-start challenges for new users or new items without degrading experience.
We implement matrix factorisation, neural collaborative filtering, and content-embedding approaches depending on your data density and latency requirements. Systems are A/B tested against your existing recommendation logic with clear revenue and engagement metrics so you can quantify the uplift before full rollout.
Recommendation models decay as user preferences evolve and catalogue changes. We build automated retraining pipelines triggered by data drift detection or scheduled windows, with shadow evaluation before each new model version promotes to production. Version management and rollback capabilities are built in from the start.
Book a free ML scoping session. We'll evaluate your data, identify the highest-impact model use case, and outline an approach you can take to your leadership team.