Make low-cost, token-efficient web + docs search for LLM agents

Make low-cost, token-efficient web + docs search for LLM agents targets a $6.0B = 2M businesses × $3K ACV (developer + internal search + RAG tooling across SMBs and mid-market averaging $3K/year) total addressable market with medium saturation and a year-over-year growth rate of 15% YoY (enterprise search and RAG adoption growth; source: industry analysis of vector DB and enterprise search adoption, 2023–2025).

Key trends driving demand: Trend — RAG and agent-based workflows are mainstreaming, driving demand for compact, high-precision retrieval that reduces LLM inference costs.; Trend — Vector databases and embeddings have matured, enabling semantic search to replace keyword-only approaches for documentation and developer search.; Trend — Growing use of single-page apps and JS-heavy documentation sites increases the need for headless rendering and DOM-aware extraction.; Trend — Teams prefer managed, integrated pipelines over assembling multiple open-source components due to reliability and maintenance costs..