Most AI projects fail. Not because the models are bad, but because the data feeding them is unreliable, inconsistent, or simply missing.
The data problem
When we talk about “the foundations for your AI,” we mean the entire stack of infrastructure that sits between your raw data sources and your intelligence layer:
- Ingestion pipelines that handle schema drift, late-arriving data, and format mismatches
- a lakebase layer that provides reliable, versioned storage with ACID guarantees
- A data fabric that creates a unified semantic view across disparate systems
- Governance and lineage so you know where every number came from
Why this matters now
As organisations rush to adopt AI, the gap between “works in a notebook” and “works in production” is becoming painfully clear. The difference? The foundations.