PageIndex provides human-like document intelligence designed to deliver precise and verifiable answers from complex documents. The platform uses reasoning-based retrieval that operates without the need for embeddings, chunking, or vector databases, ensuring all insights are grounded in the source material.
Key offerings include explainable and traceable information for individual users, vectorless retrieval via MCP and API for developers, and secure, auditable deployments with full context traces for enterprise environments.
Key offerings include explainable and traceable information for individual users, vectorless retrieval via MCP and API for developers, and secure, auditable deployments with full context traces for enterprise environments.
PageIndex is a reasoning-based retrieval engine designed to navigate long professional documents by building hierarchical tree structures similar to a table of contents. Unlike traditional vector-based systems that rely on semantic similarity through artificial text chunking and vector databases, this method enables Large Language Models to perform context-aware searches via agentic tree traversal. This approach mimics human expertise in document navigation, providing traceable and explainable results grounded in specific page and section references.
* Replaces approximate vector similarity with reasoning-driven retrieval
* Eliminates the need for artificial text chunking or vector databases
* Provides high traceability through explicit document structure grounding
* Achieved 98.7% accuracy on the FinanceBench benchmark
* Replaces approximate vector similarity with reasoning-driven retrieval
* Eliminates the need for artificial text chunking or vector databases
* Provides high traceability through explicit document structure grounding
* Achieved 98.7% accuracy on the FinanceBench benchmark
