From architecture to practical use.

An AI knowledge system needs more than a useful answer. It needs a clear boundary around the information it can use and the people who can retrieve it.

I designed and put into use a governed retrieval-augmented generation architecture for an enterprise environment. RAG connects an AI assistant to a defined collection of information. My work brought that capability together with role-based access, least privilege and an operating model for organizational use.

I also developed the rollout approach for content preparation, metadata, access testing, pilot feedback, training and support. The architecture was put into use; the rollout plan describes additional work to sequence and validate as adoption develops.

The practical lesson: decide what information belongs in the system and who should be able to retrieve it alongside the technology choices.

Cloud services and collaboration depend on knowing who a user is and which resources they should be able to access.

My implementation experience includes building new Microsoft Entra ID tenants and business-to-business federation. This sits within a broader background in cloud foundations and identity architecture, including role-based access and Conditional Access.

I bring that experience to AI and integration work by considering identity and permissions as part of the system design: who needs access, for what purpose, and how the operating team will manage it.