Common questions

Our methods were designed for enterprise constraints — security, governance, auditability, complex stakeholder landscapes — but the delivery patterns work at any scale. We typically engage with teams of 10+ in data or analytics, regardless of company size. If you have a scrappy team trying to build the right foundations early, we're a good fit.

Snowflake, BigQuery, Databricks, dbt, Airflow, Spark, Kafka, Looker, Tableau, Power BI, Cube, MLflow, and the surrounding orchestration, quality, and cataloguing tooling. We adapt to your existing stack rather than imposing ours. If you're evaluating platforms, we can help with that too — we'll give you an honest view based on your specific constraints.

Primarily project-based. We scope engagements around specific outcomes — a governed metric layer, a production ML pipeline, a data platform migration — and deliver end-to-end. We don't place individual engineers on a time-and-materials basis. This keeps us accountable for the outcome, not just the hours.

We sign NDAs as a standard part of every engagement. We never reference client data, systems, or business context outside the engagement. Our case studies use generalised industry patterns — no client names, no specific business metrics, no identifiable details.

Every engagement ends with: documented architecture decisions (ADRs), a data dictionary for every dataset we build, runbooks for every on-call scenario, a knowledge transfer session with your team recorded for future reference, and a 30-day support window for questions after the engagement closes. We measure success by whether your team can operate what we built without us.

Discovery and design phases typically run 2–4 weeks. Delivery phases range from 6 weeks for a focused build (a specific pipeline or model) to 4–6 months for a comprehensive data platform programme. We scope in phases so you can evaluate before committing to the full programme.

Yes. We've delivered modernisation programmes for organisations moving from on-premise data warehouses to cloud platforms. We design migration paths that keep existing reporting running while new infrastructure is built alongside it — no big-bang cutover.

Yes, and we do this regularly. We can assess an existing data platform, identify gaps, and produce a prioritised remediation plan — without being defensive about the original implementation. The assessment usually takes 2–3 weeks and delivers a clear picture of what's working, what's fragile, and what needs to change.