Six things we build, well.
Every service below is grounded in delivery across UK public sector, healthcare and pharmaceuticals, insurance, motor services, consumer data, and food retail. We size engagements to fit — a four-week strategic review, a six-month platform build, or an ongoing retainer once you're live.
Data Platform Engineering
Cloud-native lakehouse and warehouse environments built from scratch. We've stood up Microsoft Fabric and Azure Synapse platforms from a blank workspace through to a governed, multi-environment deployment that the rest of the business can build on.
- Dev / Test / Prod workspaces, lakehouses and enterprise warehouse structures
- Medallion architecture (Bronze / Silver / Gold) with parallel warehouse models
- CI/CD for Fabric artefacts and data engineering workflows
- Enterprise governance: data modelling conventions, lineage, access controls, quality principles
Stack we reach for
Microsoft Fabric Azure Synapse OneLake Azure Data Lake DevOps Terraform
Typical engagement
6–12 weeks for a greenfield platform; 4 weeks for an architecture review and roadmap.
Data Warehouse & Modelling
The bit that decides whether your analytics are trustworthy a year from now. We design dimensional models that hold up under real usage — including historical accuracy via slowly-changing dimensions when the business needs to ask "what did we know, when?".
- Kimball-style fact and dimension schemas, conformed dimensions across business units
- Surrogate key strategies that decouple your warehouse from source-system IDs
- Type-2 SCDs for historical reporting and reproducible point-in-time queries
- Documentation analysts can actually read
What you get
A modelled gold layer with fact/dim schemas, surrogate keys, SCD2 history where required, and a model dictionary describing every column's source, grain and intended use.
Pipelines & Integration
ETL and integration work that holds up at scale. We've built pipelines processing hundreds of millions of records, reduced multi-day workflows to minutes, and wired together messy source landscapes — multiple CRMs, third-party APIs, on-prem legacy systems — into a single coherent flow.
- Azure Data Factory, Databricks, PySpark, Airflow, DBT
- Multi-CRM and multi-source integration with reliable contracts and retries
- Third-party API ingestion (financial reporting, geospatial, identity)
- Audit databases for automated testing, error handling and overdue-ETL detection
Performance posture
Past engagements have moved batch jobs from days to under an hour by switching from row-oriented orchestrators to PySpark, and reduced legacy ETL processing time to ~10% of baseline. Performance is a deliverable, not a bonus.
Cloud Migration & Modernisation
Legacy on-prem warehouses, Alteryx + Excel workflows, hand-rolled SQL Server stacks — we migrate them to modern cloud platforms without losing the business logic baked into the originals. The hard part is rarely the technology; it's preserving institutional knowledge.
- Legacy ETL → Fabric / Synapse / Databricks
- Spreadsheet-driven reporting → governed Power BI / dimensional models
- Site-based / silo'd reporting → business-unit or enterprise-wide views
- Source-system retirement with parallel-run validation
How we de-risk it
Parallel-run reconciliation, value-by-value diffs, and explicit cutover plans. Migrations land on a chosen date — not "when it's ready".
Forecasting & MLOps
Statistical forecasting with the boring, well-understood algorithms that actually work, plus the production plumbing that gets a model out of a notebook. We've built short-term demand forecasting systems with feature engineering on weather and seasonality, and the dev-to-prod automation around them.
- Time-series forecasting (SARIMAX, exponential smoothing, gradient-boosted hybrids)
- NLP for classification, topic modelling and complaints categorisation
- Propensity modelling (XGBoost) on consumer datasets
- Model lifecycle automation: training, registry, deployment, monitoring
Stack we reach for
pandas scikit-learn statsmodels XGBoost PySpark MLlib Airflow MLflow
Data Products & Custom Software
When the answer isn't another dashboard, we build the thing itself. Productised analytics offerings (like SiteScout), bespoke ERP systems on Django + PostgreSQL, and web applications that fit a workflow no SaaS product covers.
- Productised analytics deliverables: cleaned datasets, modelled outputs, branded reports
- Custom ERP systems on Django + PostgreSQL, deployed on Linux VPS for control and cost
- Internal tools and labelling apps to support analyst-in-the-loop workflows
- API-first design when you'll integrate further down the line
Our reference product
SiteScout — productised UK food-establishment catchment analysis. Five packaged tiers, from indie operators to enterprise chains. See it →
Looking for the right shape of engagement?
Tell us what's on your roadmap. We'll suggest something honest — including "you don't need us for this" when that's the right answer.