PageIndex offers a reasoning‑based AI platform that extracts precise, verifiable answers from long, complex documents without using embeddings or vector databases. By indexing documents as lightweight JSON trees, it streams answers grounded in exact page, line, or section references and can natively interpret tables, charts, and images, enabling cross‑document analysis with enterprise‑grade security and flexible deployment options.
Funding
Funding not disclosed
Founders
Product
Problem
Professionals and enterprises often need to extract precise information from lengthy, complex documents such as legal contracts, financial reports, technical manuals, and research papers. Traditional AI tools rely on embeddings and vector databases, which can miss domain‑specific relevance, produce hallucinations, and lack traceable source references, making it difficult to verify answers and conduct cross‑document analysis.
Solution
PageIndex provides a reasoning‑based document AI that retrieves information during generation using logical relevance rather than semantic similarity. The system streams answers that are directly grounded in the source, citing exact page or section lines, and can interpret tables, charts, and images natively. By operating on a lightweight JSON tree index, it eliminates the need for separate embedding pipelines or vector databases, reducing infrastructure complexity. Users can query single or multiple documents, enabling cross‑document comparisons and deeper insights while maintaining full auditability. The platform offers cloud, on‑premise, or hybrid deployment with end‑to‑end encryption and fine‑grained access controls for enterprise security.
Target Audience
Primary customers are professionals and teams who work with long, domain‑specific documents—such as lawyers, financial analysts, researchers, and engineers—and enterprises that require secure, auditable AI‑assisted document analysis.
Features
- Logical‑reasoning retrieval integrated into the generation step, delivering immediate streaming responses
- Traceable answers with explicit line, page, or section references, eliminating hallucinations
- Native understanding of tables, charts, figures, and images within documents
- Cross‑document analysis that connects information across multiple files for pattern discovery
- Vector‑free architecture using a lightweight JSON tree index, removing the need for embedding pipelines or vector databases
- Enterprise‑grade security: end‑to‑end encryption, fine‑grained access controls, and on‑premise or VPC deployment options
- API and developer tools (MCP) for easy integration into existing workflows and custom applications