Forecasting
Models that estimate demand, volumes or other measures to support planning.
- Time-series analysis
- Scenario comparisons
- Forecast accuracy tracking
Intelligent Systems
Use Your Data to Anticipate What Comes Next.
We help teams build machine learning models for forecasting, classification and anomaly detection, and put the results in front of the people making decisions.
Where It Helps
Common Situations We Help With.
Machine learning is worth exploring when you have historical data and decisions that repeat often enough to learn from.
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Planning relies on spreadsheets and judgement alone
We use your historical data to build forecasts that planners can compare against their own estimates.
Forecasting -
Teams need help deciding which cases to handle first
We build scoring models with explainable outputs, so people can see why a case ranks higher.
Classification & Scoring -
Issues are often spotted only after they cause problems
We set baselines for normal activity and flag unusual patterns for your team to review.
Anomaly Detection -
An existing model is in use but no one is checking its accuracy
We put monitoring in place to track performance as data changes and plan when to retrain.
Model Deployment & Monitoring
Capabilities
What We Can Help You Deliver.
We focus on models people can understand and act on, not only on technical scores.
Classification & Scoring
Models that sort, rank or score records to help teams prioritize their attention.
- Feature engineering
- Model training and validation
- Explainable outputs
Anomaly Detection
Identifying unusual patterns in transactions, operations or system data for review.
- Baseline and threshold design
- Alerting workflows
- Review and feedback loops
Model Deployment & Monitoring
Moving models into everyday use and checking that they keep performing as data changes.
- Deployment into applications
- Performance monitoring
- Retraining plans
How We Work
How an Engagement Comes Together.
Model work depends on the data, so we check its quality and fit before committing to an approach.
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Phase 01
Understand the Decision
We clarify what will be predicted, who will use it and how it changes their work.
OutcomeA clear prediction target and success measure
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Phase 02
Prepare the Data
We gather, clean and explore historical data to see what it can support.
OutcomeA prepared dataset and a view of its limits
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Phase 03
Train & Validate
We build candidate models and test them on data they have not seen.
OutcomeA validated model with explained results
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Phase 04
Deploy & Monitor
We put the model into everyday use and track how it performs over time.
OutcomePredictions in use, with monitoring in place
Related Services
Often Combined With.
How much historical data do we need?
There is no single answer; it depends on the problem and how much the data varies. Reviewing your data early tells us whether it can support a useful model.
Can people understand why a model made a prediction?
Where it matters for the decision, we favour approaches and reporting that show the main factors behind a prediction, so users can judge when to rely on it.
What happens when our data changes over time?
Models can lose accuracy as conditions change. We set up monitoring and agree a plan for review and retraining as part of deployment.
Intelligent Systems
Want to Put Your Data to Work?
Tell us about the decision you want to improve and the data behind it, and we can discuss what a model could realistically support.