Know what happens next before it happens.
Typical demand forecast error margin achieved
Average reduction in stockout and overstock events
Earlier churn detection vs lagging indicator tracking
AI models that predict future demand using historical patterns, seasonality, promotions, and external market signals, updated automatically as new data arrives.
Customer health scores updated continuously, with automated alerts when risk thresholds are crossed and recommended intervention actions surfaced to your team.
Automatic identification of unusual patterns in operations, finance, or customer behaviour, catching issues before they become crises.
What-if analysis tools that let leadership model the business impact of pricing changes, capacity decisions, and market shifts before committing.
Deployed a demand forecasting model across 8,000 SKUs for a fashion retailer, reducing overstock by 35%, cutting stockouts by 42%, and freeing £2.1M in working capital previously tied up in excess inventory.
Built a weekly churn risk scoring model for a B2C subscription service, enabling the retention team to intervene 6 weeks earlier than previously, reducing monthly churn rate from 4.2% to 2.9%.
Implemented a real-time anomaly detection model processing 50,000 transactions per day for a payment processor, detecting fraudulent patterns with 94% precision and reducing false positives by 60% vs the previous rule-based system.
We bring deep domain knowledge across these sectors
Define what you want to predict, the business decision it informs, and the success metric.
Evaluate data availability, quality, and the features most predictive of the target outcome.
Train and validate predictive models, iterating until accuracy targets are met.
Deploy models into production with monitoring, retraining schedules, and business system integration.
Accuracy depends on data quality, volume, and the complexity of what is being predicted. Demand forecasting typically achieves 85–95% accuracy. Churn prediction models commonly achieve 75–90% precision. We set realistic targets in discovery.
Most models benefit from monthly or quarterly retraining as new data accumulates. We build automated retraining pipelines so models stay accurate without manual intervention.
Typically 18–24 months of historical sales data, plus any relevant external signals like seasonality, promotions, and market events. We assess your data in discovery and advise on what is sufficient.
Yes. Prediction outputs can feed directly into your ERP, CRM, inventory system, or BI tool via API or direct database integration, so model output flows into the decisions it is designed to inform.
Our forecasting models are built with seasonality decomposition, holiday calendars, and promotional uplift modelling built in. For one-off events we support manual override capability so your team can apply domain knowledge the model cannot capture.
No model is 100% accurate. We build confidence intervals into every prediction so your team knows the uncertainty range, not just a point estimate. We also track prediction vs actual outcomes over time to identify when model performance is degrading.
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