Digital Transformation before the AI.

FAQ

About this service.

• Do we need a lakehouse before doing any AI?+

  No — and anyone who says otherwise is selling one. Narrow AI use cases can ship against existing systems. But every scaled deployment eventually hits the same wall: inconsistent data, no lineage, no trusted metrics. The assessment tells you the minimum viable foundation for your roadmap — sometimes a lakehouse, sometimes three pipelines and a semantic layer.
• Azure, AWS, or GCP?+

  Whichever you're already committed to — the medallion pattern ports across all three. We've shipped an Azure Medallion lakehouse in production for an EU enterprise customer and sized estates on AWS and GCP. Migrating clouds is almost never the highest-value move; using the one you have properly usually is.
• Classical ML or GenAI — how do you decide?+

  By the shape of the problem. Forecasting, scoring, anomaly detection, optimisation — classical models win on cost, latency, and explainability. Language, synthesis, and interface problems — GenAI. Most transformations need both, and the same lakehouse feeds either. We are deliberately unromantic about this.
• Our data quality is bad. How bad is too bad?+

  It's never too bad to start; it's usually too bad to skip the audit. Quality gets fixed at the pipeline — with contracts, tests, and lineage — not by cleaning spreadsheets forever. The assessment quantifies where the rot is and which fixes unlock the most downstream value.
• How does this connect to the AI services?+

  This is the foundation they stand on. RAG needs governed, current documents; agents need reliable APIs and clean state; analytics agents need a semantic layer. The transformation is sequenced so AI workloads light up as their foundations land — not after a multi-year replatform.
• Can you work alongside our existing SI or internal data team?+

  Yes — that's the usual shape. We set the architecture, the standards, and the review gates; your team or SI does the volume build; we stay accountable for the outcome. It keeps senior judgment concentrated where it matters and the day-rate bill honest.