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We examine the inherent limitations of OCR from an information-theoretic perspective and show why a direct, vision-based approach with PageIndex is more effective.

Experience the power of reasoning-based RAG with PageIndex Chat - our new conversational interface for intelligent document understanding.

PageIndex is a vectorless, reasoning-based retrieval framework that simulates how human experts extract knowledge from complex documents. Instead of relying on vector similarity search, it builds a tree-structured index from documents and enables LLMs to perform agentic reasoning over that structure for context-aware retrieval. The retrieval process is traceable and interpretable, and requires no vector DBs or chunking.

We explore the rise of agentic retrieval over vector indexing and how PageIndex can be used to build agentic RAG systems.

PageIndex OCR is the world's first OCR model that understands documents as a whole — preserving full structure and section hierarchy across pages, instead of treating each page as an independent unit.