01
Customer & churn models
Support retention teams with customer segments and churn scores.
- Data - customer history and outcome definitions
- Evaluation - baseline and intervention timing
- Decision - cases to review for retention
Machine learning
Prediction, scoring and forecasting across the enterprise.
DATA & AI
Find the patterns that inform your decisions
Use machine learning to estimate future outcomes, score risk and detect unusual patterns in enterprise data. Start from a decision, compare models with a meaningful baseline, and validate performance before operational use.
Churn, demand, fraud, loan default and equipment failure each need different data and evaluation choices. More data or retraining does not automatically make a model better; review performance, limitations and the cost of a wrong prediction.
What we deliver
Prediction, scoring and forecasting
Use customer, operational and financial data to support retention, investigation and planning. Agree decision ownership, model outputs and evaluation needs before development.
02
Operational anomaly detection
Highlight unusual operational patterns for investigation by an accountable team.
- Data - expected patterns and known incidents
- Evaluation - false alerts and missed signals
- Decision - what needs investigation
03
Fraud & risk scoring
Support fraud and credit teams with scores for review, including loan default risk.
- Data - reviewed labels and outcome windows
- Evaluation - precision, recall and error cost
- Decision - investigation or further review
04
Demand & maintenance forecasting
Give planning and maintenance teams forecasts or failure estimates with stated limits.
- Data - demand or equipment history
- Evaluation - time-aware tests and forecast error
- Decision - planning or maintenance action
DELIVERY APPROACH
Evaluate carefully and use predictions responsibly
Agree the decision, baseline and cost of error. Define development, integration and operational scope without treating explainability as evidence of accuracy.
Custom models and reusable IP
Machine learning models built around your decision
Our ML service defines, adapts, evaluates, integrates and operates models around your decision within an agreed scope. Reusable accelerators provide selected starting points, not validated results for your data.
Predictive Analytics Suite
Reusable starting models for churn and segmentation, demand forecasting, fraud and anomaly detection, and loan default prediction.
Digital Twin Suite
For asset models, forecasting simulation, inventory optimisation and real-time monitoring. Scenario simulation is a separate scope; an ML model is not automatically a digital twin.
Power BI + AI/ML
Present evaluated model outputs through predictive dashboards and risk-scoring visuals. Data platforms and reporting work can support this presentation layer.
Confirm the reusable starting point
Agree data readiness, adaptation, evaluation, deployment and commercial scope. Accelerators are reusable delivery IP, not automatic inclusions or a substitute for validating your model.
Illustrative engagement pattern
A prediction evaluated in context
For a demand-planning model, agree the evidence below before operational use. This is an example evaluation pattern, not a client case or an achieved performance claim.
01
Baseline and evaluation record
Define the planning horizon, historical data window and baseline forecast. Use an appropriate time-aware evaluation split; record forecast error and the limits of historical data.
02
Controlled operational use
Agree a model/evaluation record, permitted data access and planner review. Scope the scoring interface and security validation before operational predictions are used.
03
Monitoring and decision ownership
Name drift, retraining and rollback owners. Review technical performance separately from business impact, with the agreed planning workflow and operating evidence.
Start with a clear question
Describe the outcome to predict or score, available historical data and the cost of a wrong prediction. Do not upload confidential datasets through the enquiry form.
BUYER QUESTIONS
Check feasibility before committing to a model
Agree an initial feasibility scope and the measures that will inform a go/no-go decision. Select commercial and support arrangements around confirmed responsibilities.
Let’s connect
Tell us about your prediction and scoring priorities
What happens next
- We route your enquiry to the relevant portfolio specialist.
- Where needed, we agree an NDA before you share confidential information.
- We clarify your priorities and agree the right next step with you.