Reduce LLM token costs for code search by 10x using a tiny RAG layer

Reduce LLM token costs for code search by 10x using a tiny RAG layer targets a $6.0B = 2M engineering teams × $3K ACV (annual tooling spend for AI-assisted development workflows) total addressable market with medium saturation and a year-over-year growth rate of 30% YoY (estimated based on AI developer tools and vector DB adoption reports and venture activity).

Key trends driving demand: Rapid adoption of AI coding assistants — widespread adoption is driving recurring token costs and demand for optimization.; Explosion of vector DBs and embeddings — mature infrastructure makes RAG layers cheaper and easier to run.; Shift to consumption-based billing — teams now see token costs on invoices and prioritize predictability and savings.; Developer-first middleware adoption — teams prefer small, composable SDKs that integrate with existing workflows rather than large platform swaps..