End-to-end capability
Five integrated practices across the data and AI lifecycle.
From foundational platform engineering through to customer-facing AI applications, we cover the full stack of capability required by serious data and AI initiatives, engineered to operate in production.
AI & Generative AI
Customer-facing AI applications and internal productivity copilots, deployed with the evaluation, monitoring, and governance frameworks required for production operation.
We work end-to-end on these initiatives: framing the use case for commercial value, designing the smallest deployment that proves it, and hardening the system for production reliability and operational handover.
- LLM-powered customer support, search, and summarisation
- Internal copilots for ops, sales, and engineering teams
- Agentic workflows that automate multi-step processes
- Document intelligence and retrieval systems (RAG)
- Evaluation harnesses, observability, and safety guardrails
- Model selection, fine-tuning, and deployment strategy
- Vertex AI
- Gemini
- Agent Builder
- BigQuery
- LangChain
- Cloud Run
Data Analytics & BI
Reporting and self-serve analytics built on semantic models that hold up to organisational change, used by the business and not only by IT.
The most common analytics failure is not the visualisation layer. It is the data model underneath, which breaks each time the organisation restructures. We build semantic layers designed to survive reorganisations, so the same question returns the same answer over time.
- Executive and operational dashboards
- Self-serve analytics with a consistent semantic layer
- Embedded analytics inside customer-facing products
- Data quality monitoring and trust signals
- Migrations from legacy BI (Power BI, Tableau, MicroStrategy)
- Enablement so your team owns it after we leave
- Looker
- Looker Studio
- BigQuery
- dbt
- LookML
Data Science & ML
Forecasting, segmentation, propensity, and uplift models embedded into operational systems, where they shape decisions at the point of action.
A model that operates only in development is a research artefact, not a system. We design for production handover from day one: monitored in operation, retrained on a defined cadence, and owned by a team equipped to respond when performance drifts.
- Demand and revenue forecasting
- Customer segmentation and propensity models
- Churn prediction, uplift, lifetime value
- Marketing mix and attribution modelling
- Operational ML: anomaly detection, capacity planning
- MLOps: monitoring, retraining, drift detection
- BigQuery ML
- Vertex AI
- Python
- scikit-learn
- dbt
- Cloud Composer
Data Engineering & Platform
Modern cloud data platforms architected for scale, governance, and predictable cost across batch, streaming, and reverse-ETL workloads.
We design platforms that grow without operational surprise. That means clear data contracts at integration seams, auditable lineage, and cost observability instrumented from the start, not retrofitted later.
- Greenfield cloud data platforms and migrations
- Streaming and batch pipelines
- Reverse ETL into operational systems
- Data contracts, governance, lineage
- Cost monitoring and FinOps for data workloads
- Migrations from legacy warehouses to BigQuery
- BigQuery
- Dataflow
- Dataform
- Cloud Run
- Cloud Composer
- Pub/Sub
- dbt
UI / UX Design
Product and interface design for analytics, AI, and internal tools, focused on adoption and measured by use.
Most data and AI initiatives fail on the last mile: the interface where the system meets a human user. We design for sustained use rather than launch demonstrations, measured by adoption metrics six months on rather than the polish of the initial release.
- AI product UX: chat, copilots, agentic flows
- Internal tool design: analytics, ops, admin consoles
- Dashboard UX that drives action, not just visibility
- Design systems for data products
- Adoption research and usability testing
- Front-end build collaboration with engineering
- Figma
- Design tokens
- Prototyping
- Usage analytics
A deliberate way of running engagements.
Three operating principles that shape every engagement we take on, from a two-week discovery through to a multi-quarter platform programme.
Discovery-led
Every engagement begins with a discovery sprint that produces a costed, prioritised plan with clearly framed options. Continuation is contingent on a clear business case. If the work cannot demonstrate sufficient value, we conclude the engagement rather than continue.
Engineered for production
Every system is engineered for production from inception: tested, monitored, governed, and accompanied by the runbooks the operations team requires for on-call ownership. Delivery is not complete at the demo.
Single point of accountability
Every engagement has a named principal with end-to-end accountability, from kickoff through to production handover. One firm, one accountable team, no diffused responsibility across vendor relationships.
Have a specific outcome you would like to discuss?
A short conversation is usually enough to determine fit and to outline what a sensible first engagement might look like.