Executive Summary

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.

Market Opportunity

Feed whole codebases to LLMs — compile repositories into one AI-friendly file targets a $18.0B = 20M professional developers x $900/year average dev-tool spend total addressable market with low saturation and a year-over-year growth rate of 30%+ (developer AI tools & RAG adoption).

Key trends driving demand: LLM-driven developer workflows -- dev teams increasingly use LLMs for code search, PR summaries, and automated refactors, driving need for repo ingestion tools.; RAG adoption -- retrieval-augmented generation is standardizing how external data is supplied to LLMs, demanding reliable doc ingestion pipelines.; Vector DB commoditization -- managed vector stores and cheap embedding compute make per-repo indexes affordable for teams of all sizes.; Mono-repo & microservices growth -- larger and more distributed codebases increase the friction of ad-hoc context assembly for models..

Key competitors include Sourcegraph (Cody), LlamaIndex (GPT Index), GitHub (Copilot, Code Search), LangChain (and developer toolkits).

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

Executive Summary

Market Validation

Market Opportunity

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