Executive Summary

Many engineering and product teams struggle with noisy, token-heavy retrieval when using LLMs for developer and docs search—this drives up inference costs and latency, especially for SMBs and mid-market companies that need precise, compact context for agent workflows. The pain is most acute for teams managing JS-heavy single-page apps and living documentation where naive crawling and chunking produce too much irrelevant context. You could build a hosted developer platform that combines headless rendering, DOM-aware extraction, embeddings, and a token-efficient retrieval layer that returns compact, query-aware snippets and integrates directly with agent SDKs; pricing could be structured per-index and per-query to reward efficiency. Include turnkey connectors to popular vector stores and CI-friendly syncs so teams can plug it into existing pipelines quickly. The market is attractive now: we estimate a $6.0B addressable market (2M businesses × $3K ACV) as RAG and agent-based workflows mainstream and vector search replaces keyword-only approaches. The timing is right because the technical building blocks (embeddings, vector DBs, headless browsers) are mature, while SPAs and dynamic docs have increased demand for DOM-aware extraction. You can differentiate by optimizing for token-efficiency (potentially cutting LLM input tokens by an estimated 30–70% depending on content and strategy), low-latency retrieval, and ergonomics for agent developers; these are defensible engineering moats but require nontrivial investment in crawling, chunking, and relevance tuning. Be upfront: competition is medium from vector DBs and search startups, so success hinges on demonstrable cost savings, strong SDKs, and reliable, scalable crawling.

Market Opportunity

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..

Key competitors include Algolia, Pinecone, LlamaIndex (open-source) / community SDKs, microsoft/playwright-mcp (open-source).

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

Executive Summary

Market Validation

Market Opportunity

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