Advanced analytics and ML enablement
Move from reporting demand, risk and churn to forecasting them. We co-build the first models with your team on a single deployment path, using the governed data foundation you already run.
Models that never leave the notebook
Most organisations reach solid descriptive analytics and stop there. The models that would anticipate demand, risk or churn stay in notebooks because no repeatable path exists from an experiment to a versioned, monitored artefact the business can depend on, so each data scientist improvises one.
Enablement closes that gap. We select the use cases worth modelling, build them with your analysts against a shared feature store and deployment pipeline, and hand over the practices so the next model does not need us. The decision is not which algorithm to use, but which decisions you are prepared to hand to a model and who owns it afterwards.
From candidate use case to a model in use
Each phase leaves the team more able to do the next one alone.
Use case selection
Which questions are worth modelling, judged on value and on data readiness.
Model development
Built with your analysts on the governed foundation, with evaluation from the start.
Business validation
Measured against the decision it has to improve, not against a benchmark.
Capability transfer
Practices, templates and standards left with the team that will run it.
Why models do not make it into the business
Five obstacles between a promising notebook and a decision that changes.
Models with no decision behind themThe model is accurate and nothing in the business changes because of it.
Use cases framed by decisionWe start from the decision to improve and work back to the model it needs.
Experiments that never industrializeEvery model is a one-off, built differently, impossible to maintain.
A repeatable pathTemplates, standards and evaluation practices shared across the team.
Data not ready for modellingHalf the effort goes into finding and cleaning the inputs, every single time.
Built on the governed foundationModels consume the same catalogued, quality-checked data as everything else.
No validation with the businessPerformance is judged on technical metrics nobody in the business recognises.
Validated against the decisionSuccess measured by the change in the decision, agreed before building.
Dependency on one specialistOnly the person who built it understands it, and they eventually leave.
Capability transferredYour analysts build alongside us, so the practice stays after the engagement.
What the engagement includes
Models built with your analysts, on your governed data, against decisions you already make. Not for organisations without reliable reporting or an owned data platform; descriptive analytics has to work before predictive does.
Use case selection
Candidate questions scored on business value and on whether the data can support them.
Model development
Models built with your analysts, with evaluation and validation designed in from the start.
Industrialization practices
Templates, standards and the path from experiment to something maintainable.
Enablement
Your team trained on the way of working, not just on the tool.
Where this has already been done
Audience prediction as an automated service, not a monthly study
An audience analysis business that needed to anticipate consumer behaviour rather than report it afterwards.
We developed an automated audience prediction service using machine learning on Azure services.
Conversion improved by knowing consumer behaviour in advance.
Select your first ML use case
Half a day with your analytics leads. You leave with a shortlist of modellable use cases and a data readiness verdict for each.
Book the readiness review