Advanced Analytics & Machine Learning
Analytics teams routinely produce good models that never change anything, because the output arrives in a report rather than in the workflow, and because nobody agreed in advance what would be done differently.
We work backwards from the decision. Before building we establish who acts on the output, in which system, and what accuracy is required for the action to be worth taking.
Platforms we typically deliver this on
Scope of the service
Demand and capacity forecasting
Time-series forecasting for volumes, staffing and resource planning, with honest confidence intervals.
Segmentation and propensity
Grouping populations and estimating likelihood of outcomes for targeting, prioritisation and early intervention.
Risk and anomaly detection
Scoring for fraud, error, non-compliance and operational anomaly, tuned against the cost of false positives.
Optimisation
Scheduling, routing and allocation under real constraints, with the constraints elicited from the people who currently do it manually.
Model validation
Backtesting, sensitivity analysis, stability monitoring and clear documentation of the limits of each model.
Production deployment
Serving predictions into the operating system of record, with monitoring and retraining.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Forecasting demand for capacity planning
Planning relied on last year's actuals plus judgement. A seasonal model incorporating referral patterns and known drivers now feeds the planning cycle directly.
Outcome — Planning based on a forecast with stated uncertainty rather than a single number.
Early identification of students needing support
Engagement signals across systems were combined into a prioritisation score for advising teams — deliberately used to allocate support, never to make decisions about a student.
Outcome — Advisor time directed by evidence, with the model's role bounded by policy.
Prioritising inspection activity
Inspection scheduling was largely geographic. A risk model built on historical findings reordered the schedule by likelihood of a material issue.
Outcome — The same inspection capacity finding more of what it exists to find.
Before you get in touch
How much history do we need?
It depends on the pattern. Seasonal forecasting generally wants two to three years; classification can work with far less if the events are frequent. We assess feasibility before proposing a build.
How do you handle fairness in models about people?
Testing across relevant population segments, documentation of limitations, and a firm line on decision scope. Models that affect individuals should inform human decisions, not replace them.
What happens when a model degrades?
Monitoring detects drift, and retraining is scheduled or triggered. Where we run the system, that is covered by the managed service.
Often engaged alongside this
Custom AI & Generative AI Development
We build the AI systems that do not exist off the shelf: document processing against your forms, classifiers trained on your taxonomy, copilots that read your systems of record.
Read moreData & AnalyticsData Engineering & Pipelines
Ingestion, transformation and orchestration built with tests, lineage and alerting — so a broken feed is an alert, not a discovery three weeks later.
Read moreManaged ServicesDevOps & MLOps Managed Services
CI/CD, environment management, release automation and MLOps — the engineering platform your teams build on, run by us.
Read moreStart with three weeks and a straight answer
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