Data engineering
Ingestion and transformation pipelines across operational systems, files, APIs and event streams – with monitoring, alerting and reprocessing that works when a source system misbehaves.
Services
Data and AI work that ends in a decision, not a dashboard
We build the pipelines, the warehouse and the reporting layer – and we care more about whether the numbers get used than about how the charts look.
The common failure in data programmes is not technical. It is that a platform gets built, dashboards get delivered, and nobody trusts the numbers enough to act on them. We work backwards from the decisions you need to make.
What we deliver
Data warehouse and lakehouse
Modelled, documented storage designed for the questions your business actually asks. Modular and extensible, so the second use case does not require rebuilding the first.
Analytics and visualisation
Reporting, executive dashboards, scorecards and KPI management – built with the people who will use them, not delivered to them.
Data quality and governance
Definitions, lineage, ownership and quality rules. The unglamorous work that determines whether anyone trusts the output.
Master and reference data
Reconciling the same customer, product or site appearing five different ways across five systems.
Migration and consolidation
Moving off legacy warehouses and consolidating overlapping reporting estates, with validation that proves the numbers still tie out.
Applied AI and advanced analytics
We start from a decision someone has to make repeatedly, then work out whether a model beats the current approach. Often it does not, and we say so.
Forecasting and demand planning
Demand, inventory and capacity forecasting where there is enough clean history for a model to beat the existing planning rule. Measured against that rule, not against nothing.
Document and content processing
Extracting structured data from invoices, contracts, forms and correspondence, with a human review step wherever a wrong extraction has real cost.
Anomaly and exception detection
Surfacing the transactions, readings or claims worth a person's attention – tuned on your own false-positive tolerance rather than a generic threshold.
Customer and revenue analytics
Segmentation, churn and propensity models, and the plumbing to get scores back into the systems where someone acts on them.
Operational AI assistants
Retrieval over your own documentation and data, with citations, so answers can be checked. Scoped to defined use cases rather than deployed as a general chatbot.
Model operations
Deployment, monitoring for drift, retraining schedules and rollback. A model without this is a prototype, whatever it is called.
Start small, deliberately
We would rather deliver one decision-support use case end to end in eight weeks than a platform in nine months that nobody has used yet. The first use case proves the pipeline, the model and the governance – and gives you something real to show the people funding it.
Then extend
Once the foundation is proven, additional use cases are incremental. That is where the economics of a data platform actually show up. The mistake is spending the whole budget getting to that point.
Cannot get a straight answer out of your own data?
Tell us the question the business keeps asking. We will tell you what it takes to answer it reliably.