Decisions backed by data. Not instinct.
Machine learning models built on your historical data to forecast demand, identify risk, and surface opportunities. Your data contains patterns your team cannot see. We build models that find them.
Predictive models for every business challenge.
From demand forecasting to churn prediction, we build machine learning models that improve the decisions your business makes every day.
Demand and Sales Forecasting
Machine learning models trained on your historical sales, seasonal patterns, and external factors to produce accurate demand forecasts. Reduce overstock, prevent stockouts, and plan resources with confidence.
Customer Churn Prediction
Models that identify customers at risk of leaving before they do. Churn probability scores updated continuously, enabling your team to intervene with the right customers at the right time.
Risk Identification and Scoring
Predictive risk models that score customers, transactions, suppliers, or projects against your historical risk data. Identify high-risk situations early and allocate oversight resources accordingly.
Revenue and Margin Optimisation
Models that identify pricing opportunities, margin improvement levers, and revenue risks across your customer base and product portfolio. Decisions backed by data rather than intuition.
Predictive Maintenance
For organisations with physical assets, AI models that predict equipment failure before it occurs. Maintenance scheduled based on predicted need, not fixed intervals, reducing downtime and cost.
Operational Performance Forecasting
Models that forecast operational KPIs including throughput, capacity utilisation, and service levels. Plan staffing, resources, and capacity based on predicted demand rather than historical averages.
From business problem to deployed model.
Business Problem Definition
We define the specific decision you want to improve, the outcome you want to predict, and the value of getting that prediction right. Commercial justification established before any technical work begins.
Data Assessment and Preparation
We assess the quality, volume, and completeness of your historical data. Data cleaning, feature engineering, and gap identification completed before model development.
Model Development and Validation
Multiple model approaches tested against your data. The best-performing model validated against held-out data to ensure it generalises to new situations, not just historical patterns.
Business System Integration
Predictions delivered where decisions are made. CRM, ERP, dashboards, or direct API integration so your team sees the model output in their existing workflow.
Team Training and Adoption
Your team trained on how to interpret and act on model outputs. Confidence intervals, model limitations, and override procedures explained clearly.
Model Monitoring and Retraining
Ongoing monitoring of model accuracy as new data accumulates. Regular retraining to maintain performance as your business and market conditions evolve.
Predictive analytics improving UK businesses.
Retail and Distribution
A regional distributor was carrying 35% excess stock on slow-moving lines while experiencing stockouts on fast-moving products. Demand planning was based on last year plus a percentage uplift.
Demand forecasting model built on 3 years of sales data. Forecast accuracy improved from 61% to 89%. Excess stock reduced by 28%. Stockout incidents reduced by 74% in the first 6 months.
Financial Services
A lender was experiencing higher-than-expected default rates on a specific product. The existing credit scoring model had not been updated in 4 years and was not capturing new risk patterns.
Updated risk scoring model built on recent default data. Default rate on new originations reduced by 31%. Model now updated quarterly with new performance data.
Professional Services
A consultancy was losing clients at a rate that was difficult to predict or prevent. By the time clients indicated they were leaving, it was too late to intervene effectively.
Churn prediction model built on engagement, billing, and communication data. At-risk clients identified 8 weeks before typical churn point. Retention rate improved by 22% in the first year.
Ready to build your predictive model?
Book a free data assessment. We will review your historical data, identify the most valuable prediction to build first, and give you a clear picture of what is achievable before any commitment.