PageIndex is a vectorless, reasoning-based RAG (Retrieval-Augmented Generation) engine that mirrors how humans read documents. It delivers traceable, explainable, and context-aware retrieval without vector databases or chunking.

How is PageIndex different from vector databases?

Unlike vector databases that rely on semantic similarity matching, PageIndex uses logical reasoning to understand and retrieve information from documents. This approach provides exact page references and explainable retrieval decisions, making it ideal for domain-specific documents where accuracy is critical.

What accuracy does PageIndex achieve?

PageIndex achieves 98.7% accuracy on the FinanceBench benchmark, the highest in the market. This benchmark tests RAG systems on complex financial document analysis tasks.

Does PageIndex require chunking documents?

No, PageIndex does not require chunking. Traditional RAG systems split documents into chunks for vector embedding, which can lose context. PageIndex processes documents holistically using reasoning-based retrieval.

What types of documents is PageIndex best for?

PageIndex is best for domain-specific document analysis including financial reports and SEC filings, regulatory and compliance documents, healthcare and medical reports, legal contracts and case law, and technical manuals and scientific documentation.

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Get verifiable answersfrom complex documents

Get verifiable answers
from complex documents