The article argues that while engineering teams may be technically capable of building an internal AI context layer, doing so often becomes a significant ongoing burden due to the complexity of maintaining a governed, trustworthy knowledge pipeline. It outlines five core stages such a system must handle: capture, deliver, govern, organize, and validate. Capture involves not just connecting to data sources but maintaining those integrations as APIs evolve, and transforming raw documents into durable knowledge nodes with metadata like ownership and trust levels. Validation is critical, as treating all content equally leads to unreliable AI outputs; it requires both implicit trust (automated scoring based on signals like authorship and recency) and explicit trust (human-in-the-loop review for high-stakes scenarios). Organize assigns knowledge to appropriate scopes with inherited permissions, preventing sensitive or irrelevant content from leaking across teams. Govern ensures compliance by integrating with enterprise security tools and providing audit trails. Deliver makes validated knowledge accessible through standardized interfaces like the Model Context Protocol (MCP), enabling both query-based and proactive knowledge delivery. The author concludes that unless knowledge infrastructure is a competitive differentiator, most organizations should avoid building these systems in-house and instead adopt platforms designed to manage the compounding operational costs of trust, governance, and maintenance at scale.