AI services
Predictive analytics & machine learning
Not every AI problem needs a language model. Forecasting demand, flagging fraud or recommending products are classic machine learning tasks, and well-built models for them are fast, cheap to run and easy to explain.
Last updated
What's included
- Time series forecasting
- Fraud detection
- Demand prediction
- Recommendation engines
What is predictive analytics?
Predictive analytics uses historical data and machine learning models to estimate what is likely to happen next, such as next month's demand, the chance that a transaction is fraudulent or which customers may cancel. The predictions feed everyday decisions in planning, risk and customer management.
Where it helps
Common uses for predictive analytics
We build models on your historical data, measure them against simple baselines and connect the predictions to where decisions are made, such as your ERP, CRM or dashboards.
Demand and sales forecasting
Better stock, staffing and budget planning from historical patterns and seasonality.
Fraud and anomaly detection
Flag unusual transactions, claims or sensor readings for review.
Recommendation engines
Suggest products, content or next actions based on behaviour.
Churn and risk scoring
Identify customers or accounts that need attention before it is too late.
How we deliver
From first call to production
- 01
Frame the decision
We agree which decision the prediction supports and what a wrong prediction costs.
- 02
Prepare the data
We combine and clean your data sources and build the features the model learns from.
- 03
Model and validate
We compare approaches on held-out data against simple baselines, so the gain is real.
- 04
Deploy and retrain
We deliver predictions by API or in batches, monitor for drift and retrain on schedule.
Technology we use
- Python
- PyTorch
- TensorFlow
- MLflow
- PostgreSQL
- MongoDB
- AWS
- Azure
FAQ
Questions about predictive analytics
How much historical data do we need?
For forecasting, usually one to two years so the model can learn seasonal patterns. For other problems it depends on how often the event you're predicting happens. We check your data during discovery.
Can we see why the model made a prediction?
Yes. We favour models and tools that show which factors drove each prediction, which matters for credit, pricing and other regulated decisions.
What happens when our business changes?
Models drift as conditions change. We monitor accuracy in production and retrain on new data on a schedule or when performance drops.
Do we need a data warehouse first?
No. We can start from your existing databases and exports and recommend data improvements as part of the project.
Free consultation
Talk to us about predictive analytics
Book a free 60-minute strategy session. We'll look at your use case, check feasibility and recommend an approach.
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