Feed whole codebases to LLMs — compile repositories into one AI-friendly file

Modern LLM-assisted workflows break down when teams try to feed an entire repository into a model: context windows are limited, source is fragmented across files and branches, and ad-hoc ingestion pipelines produce stale or noisy indexes that generate incorrect suggestions. This problem is most acute for professional developer teams—roughly 20 million developers globally—who currently spend on average $900/year on dev tools, creating an $18.0B addressable market where poor repo ingestion reduces productivity across code search, PR review, and automated refactors. A practical product would deterministically compile a repository into a single AI-friendly artifact that preserves syntax trees, dependency graphs, test surfaces, and metadata while emitting chunk mappings and precomputed embeddings for RAG workflows; it would support incremental updates in CI, on-prem deployment for security-sensitive customers, and standardized SDKs for major LLM and vector-store providers. The implementation should optimize tokenization, deduplicate repeated assets, and expose provenance so model outputs can be traced back to exact files and commits. This market is attractive now—LLM-driven developer tooling and RAG have become standard design patterns, vector DBs and embedding compute are commoditizing, and the market score of 90/100 with revenue potential 84/100 reflects strong willingness-to-pay; competition is currently low but strategic risks are real. The product can stand out by offering a canonical, deterministic compilation format that reduces indexing cost and query latency, integrated CI freshness, and enterprise-grade privacy controls, while being honest about challenges such as scaling to extreme monorepos, embedding costs, and the possibility of cloud incumbents replicating the idea.