PageIndex

PageIndex is a document intelligence platform that takes a deliberately contrarian approach to RAG: no embeddings, no chunking, no vector database. Instead it uses a reasoning-based retrieval pipeline that navigates documents structurally and returns answers grounded in cited source pages. The product ships as a hosted chat interface, a developer API, and an MCP server, plus an open-source component on GitHub.

The pitch lands hardest for teams working with long, structured PDFs - financial filings, legal contracts, regulatory dossiers, technical manuals - where chunk-and-embed pipelines lose context and hallucinate. PageIndex's selling point is auditability: every answer comes with a traceable path back to the page it came from, which matters for compliance-bound workflows. There is a free Try Now tier; pricing for higher usage and enterprise deployments is not published and goes through a demo booking.

The vectorless approach is the differentiator and the caveat in one. It sidesteps the well-known failure modes of similarity search but it is a newer architecture than mainstream RAG stacks, so prior art and community recipes are thinner. The MCP server and cookbook docs make it reasonable to drop into an existing agent setup without rebuilding a retrieval layer from scratch.