Most AI projects fail not because of models, but because their content foundations do not scale. Vector stores fill up, retrieval quality degrades, access control breaks, and "AI-ready data" turns into operational debt.

This session presents a practical architecture for building an AI-ready content lake that supports Retrieval-Augmented Generation and document intelligence at enterprise scale. Using the newly released open source Cloud Content Repository (CCR) as a real production example, the talk focuses on engineering decisions rather than product features.

We will walk through how to ingest, structure, enrich, and index billions of unstructured documents while keeping latency, cost, and security under control. The session covers hybrid retrieval strategies, metadata-aware embeddings, permission-safe access, and observability patterns that keep AI systems reliable in production.

CCR is used as a concrete implementation, but the architectural patterns apply equally to custom repositories, object storage–based lakes, and platform-agnostic AI stacks. Attendees will leave with a clear mental model for when RAG works, when it fails, and how to design content pipelines that survive real users and real data.