Reduce LLM token costs by indexing code for retrieval instead of re-reading files

Many engineering teams and platform organizations are now paying for LLM context repeatedly because models are asked to re-read large files and entire monorepos instead of retrieving precise snippets; this impacts both startups and enterprises, and is meaningful given a developer population of ~20M and a $36B dev-tools/AI market ($1,800/year per developer). Token-costs compound as LLMs are embedded across code review, generation, and maintenance workflows, so even modest per-query savings scale quickly across teams. You could build a focused retrieval layer that indexes code with AST- and semantics-aware embeddings, maintains metadata for freshness, and serves low-latency, relevance-scored snippets so models consume far less token context. Deliver SDKs and repo/CI connectors, offer both cloud-managed and on-prem privacy options, and monetize via per-seat plus usage tiers and enterprise integrations to capture high ARR potential. This is an attractive moment: LLM adoption is accelerating in developer tools, managed vector databases have matured to reduce infra friction, and monorepos are increasing the pain of naive context passing—market and revenue scores (95/100 and 94/100) reflect that opportunity. Early pilots in similar retrieval-heavy workflows report multi-tens-of-percent reductions in token usage, so ROI can be realized within weeks for heavy LLM users. To stand out, prioritize code-specific indexing (AST-aware features, cross-references, and test/dep awareness), seamless CI/IDE integration, and clear freshness semantics, and form partnerships with vector DB providers to minimize customer ops. Be honest about challenges: initial indexing and freshness infrastructure, convincing teams to change CI pipelines, and direct competition from large platform players—so start with a narrow vertical MVP and strong measurable ROI for pilot customers.