Data warehouse projects are scoped individually, because cost depends on the size of your data estate, the platform chosen and whether you are migrating off legacy systems or building fresh. We begin with a fixed-scope architecture engagement to define the design and a clear plan, then phase the build so investment aligns with delivery rather than a single open-ended commitment. We provide a written proposal scoped to your environment before any build begins, never open-ended day rates.

A sound foundation also underpins governance - see our trusted data architecture and governance approach.

If reporting is straining, refreshes are slow, or you are maintaining ageing on-premise SQL Server hardware, moving to a modern cloud platform almost always pays off. Cloud warehouses and lakehouses scale elastically, remove the maintenance and patching burden, and give you governance and security tooling on-premise systems cannot match. We sequence the migration so reporting keeps running throughout, redesigning the schema for analytics rather than simply lifting and shifting the old structure into the cloud.

A data warehouse stores structured, modelled data optimised for reporting, while a data lakehouse combines that structured layer with a data lake's ability to hold raw, granular and unstructured data in one platform. For most organisations the lakehouse is the stronger long-term choice: it serves today's reporting and keeps the detailed data that machine learning and AI need, avoiding a second platform later. We design the right balance for your needs rather than forcing one pattern.

Not every Power BI report needs a warehouse, but once you have multiple sources, large data volumes or numbers that must reconcile across the business, a warehouse becomes the foundation that makes reporting reliable. It gives Power BI fast, consistent, governed data with a single definition of every metric, instead of each report wrestling raw sources. If your Power BI reporting keeps breaking or contradicting itself, the data foundation is usually the real problem.

See how we ready that foundation on our preparing your data for AI page.

A well-architected modern data platform is the single biggest step toward AI readiness, because most AI and large language model initiatives fail on data foundations, not models. When your platform has clean ingestion, governed models, lineage and clear semantics, your data becomes discoverable and trustworthy enough for machine learning and LLM use cases. We design the foundation with that future in mind, so you are not re-engineering it the moment an AI project starts.