Over the recent past, as enterprises have realised the transformative potential of generative AI, there has been an imperative to launch functional, large-scale AI use cases. To that end, agile squads were created, model, vibe coding assistants and compute provisioned, and multi-agent demos built. But when these systems leave the sandbox and get tested on pre-production, a common bottleneck shows up. The models reason fine, but unlike the well-formed synthetic data used in demos, the underlying enterprise data is'nt accessible, clean, or structured enough for them to act on reliably.

Traditional enterprise data platforms were architected for a fundamentally different set of technologies, that of deterministic (BI dashboards) or predictive analytics (ML use cases). Data engineering evolved to include ETL pipelines, dimensional star schemas, and Lakehouse Medallion architectures (Bronze -> Silver -> Gold).…