Institutional digital asset management for 2025 centers on integrating large language models with existing document stores, catalogs and storage layers while preserving security, provenance and operational control. This topic covers the platforms and integration patterns that operationalize Retrieval‑Augmented Generation (RAG), persistent AI memory, vector search and Model Context Protocol (MCP) connectors across clouds and on‑prem environments. Key trends making this timely include wider MCP adoption for standardized tool access, demand for on‑prem and hybrid deployments to meet data‑residency and compliance needs, and growing use of combined vector and graph approaches for contextually rich memory. Representative tools include Cognee‑mcp (graph+vector memory server for persistent AI memory), Chroma (open‑source embeddings, vector search and document store with an MCP server), and Minima (containerized on‑prem RAG/MCP solution). Connector MCP servers expose enterprise sources: obsidian‑mcp for Obsidian vaults, Atlassian’s MCP server for Confluence and Jira, Notion MCP for Notion workspaces, and Supabase MCP for programmatic database and edge function interactions. Practical concerns span Document Management integrations (indexing, metadata, access controls), Data Catalog & Lineage (provenance, audit trails), Cloud Data Platforms and Cloud Platform Integrations (managed vs self‑hosted tradeoffs), Cross‑Platform File Search (unified retrieval across silos), and Storage Management Integrations (tiering, lifecycle, costs). Together, these components form interoperable stacks that enable explainable retrieval, secure agent workflows, and scalable context for LLMs while balancing governance and operational constraints.