The challenge
A global bank needed a governed MultiRAG architecture to move generative AI beyond isolated chatbots, supporting secure retrieval across enterprise knowledge sources with strict governance, security and data classification before wider platform build.
What we did
Scoped the MultiRAG architecture and solution design on Azure Databricks: enterprise RAG pipeline patterns, Foundation Model APIs, AI Gateway and model endpoints, Vector Search with reranking, Azure Content Safety, custom data classification, Unity Catalog governance, and reusable retrieval and governance controls for multi-domain generative AI assistants.
The outcome
Produced a clear MultiRAG architecture and solution-design baseline for a governed enterprise AI factory, and scoping and design direction for subsequent delivery teams, not pilot delivery or production rollout.
Contributed to the design of a governed enterprise AI factory: governed retrieval, model access and enterprise generative AI controls.
Engagement was MultiRAG architecture and solution design scoping, not pilot build or production rollout. Design covered secure model access, hybrid multi-source retrieval, reranking, guardrails and Unity Catalog controls, so multiple generative AI apps could later share one governed RAG control plane.