Predictive Modelling
Estimate likely outcomes from historical patterns and compare performance against a simple, meaningful baseline.
Machine Learning Solutions
Find the patterns. Test the possibilities.
Use your historical data to build models for prediction, classification and recommendations, with evaluation that reflects how the model will be used.
The business case
Start with a useful outcome.
A useful model does more than perform well on a development dataset. It needs relevant inputs, a credible baseline and testing that matches the business setting. We work through data quality, model selection and deployment together, so you understand both the opportunity and the limits of the prediction.
Discuss your requirementCapabilities
What we can help you build.
The scope is shaped around your systems, your information and the work you need to do.
Classification Systems
Organise records, requests or observations into useful categories, with review paths for uncertain results.
Recommendation Systems
Suggest relevant products, content or next actions based on available signals and agreed business objectives.
Forecasting
Estimate future demand or activity with attention to time patterns, changing conditions and uncertainty.
Custom Machine Learning Models
Develop and evaluate models for a specific problem when existing approaches do not adequately fit the data or task.
Practical applications
Where this can help.
Examples of suitable use cases, to help you think through your own requirements and decide whether there is a worthwhile starting point.
Demand planning
Use historical activity to support stock, capacity or resource planning.
Request classification
Help operations teams organise incoming work and focus review effort.
Relevant recommendations
Make information or product discovery more useful while testing for unintended behaviour.
A clear working agreement
Know what you are
working towards.
Deliverables are agreed before work begins. A typical engagement may include the following, adjusted to the scope and complexity of your project:
- A data quality assessment and modelling baseline
- Documented features, training and evaluation approach
- Performance results with relevant error analysis
- Deployment and model monitoring recommendations
Before we begin
A useful first conversation.
Useful inputs include historical records, the outcome you want to predict and a description of how a wrong prediction affects the business. The available data determines whether modelling is feasible.
Talk through your ideaCommon questions about getting started
What to expect before and during a project.
A credible performance target depends on the data, task and evaluation method. We establish a baseline and assess achievable performance before making a deployment recommendation.
Model performance can change as inputs or behaviour change. Monitoring, review thresholds and retraining responsibilities should be part of the operating plan.
There is no single minimum that applies to every problem. Coverage, quality, class balance and the frequency of the outcome can matter as much as the number of records.
Connected expertise
Bring the pieces together.
Data analytics & BI
Bring scattered business data into a clearer picture, with consistent measures, useful dashboards and reporting built around the decisions you make.
Explore serviceArtificial intelligence
Build AI applications around a clear business task, with the right data, practical controls and a useful place in your existing workflow.
Explore serviceInternet of things
Bring device and sensor information into applications that help teams see conditions, spot exceptions and respond with context.
Explore serviceStart a conversation
What would you like to build?
Tell us what is slowing you down, or what you want to do next. A short description of your business question is all it takes to begin.