In large-scale enterprises, tribal knowledge often stalls developer productivity and slows operational agility. Subject matter experts (SMEs) become bottlenecks when critical business logic and configuration decisions live only in legacy codebases or in the minds of a few veterans.  This article presents an architecture that leverages GraphCodeBERT embeddings, FAISS vector search, and large language models (LLMs) like Sonnet or Claude to transform complex enterprise logic into conversational explanations. Drawing on real-world use cases in asset lifecycle processing and internal platform operations, it outlines a scalable, GenAI-driven approach to overcoming SME bottlenecks and accelerating engineering velocity.