The biggest issue Part 1 left open was that AI couldn't reach the 46-repo codebase by natural-language query (the entry-point problem). This post is how I solved it — by reusing the pattern proven in db-graph (1,133-table semantic search), then layering minimal annotations only around boundary nodes. Covers the separate-branch operation that keeps engineers' daily workflow untouched, the SLO that protects the joins between three graphs, the SAME_ENTITY normalization, and the April–May trial-and-error timeline traced through real commits.
Making the Context Across 46 Repositories Semantically Searchable for AI (Part 2)
