Document retrieval without vectors — LLM-native RAG for smarter search

Document retrieval without vectors — LLM-native RAG for smarter search targets a $9.0B = 300,000 organizations × $30K ACV (enterprise and mid-market document intelligence and knowledge-layer tooling) total addressable market with medium saturation and a year-over-year growth rate of Approx 20% YoY (industry estimates for knowledge management and RAG tooling, Grand View Research / Gartner signals, 2023-2025).

Key trends driving demand: LLM capability improvements — better semantic understanding and longer context windows reduce dependence on external embedding-only pipelines, enabling innovative retrieval patterns.; Rising embedding & vector DB costs — as usage grows, teams seek cost-reducing alternatives, creating demand for retrieval approaches that lower API and storage spend.; Privacy and data-locality requirements — organizations want retrieval that minimizes external data movement and gives better governance, driving interest in architectures that avoid pushing raw vectors or broad datasets to third parties.; Developer-first adoption — engineering teams are experimenting with RAG quickly; tools that minimize infrastructure friction while offering SDKs will get rapid uptake.; Shift to hybrid retrieval — customers increasingly combine lightweight indexes with model-native reasoning, creating an opening for systems that orchestrate these layers efficiently..