PageIndex

PageIndex retrieves answers from long documents by reasoning over their structure, not by searching vectors. Its maker, Vectify AI, calls it a vectorless, reasoning-based RAG engine that mirrors how people read documents, and says it reached 98.7 percent accuracy on FinanceBench with traceable, explainable retrieval.

Retrieval without embeddings

The pitch to developers is verifiable answers from complex documents, with no embeddings and no vector database. Because the retrieval path can be traced, an answer can be tied back to the pages it came from, which matters wherever an unsourced answer cannot be used.

Page Search and Page Extract

Two APIs carry the product. Page Search picks out the pages that matter for each question, taking about a second, so a language model reads only those pages and not the whole PDF. PageIndex says this cuts token cost by 97 percent and beats state-of-the-art vector search on speed and accuracy. Page Extract takes a JSON Schema describing the fields wanted and returns them from any PDF as structured data, and the company ranks it first for accuracy on ExtractBench at 60 percent lower cost than the next best.

Agent and chat app

PageIndex Agent applies vectorless RAG to long, complex documents. A separate Chat App, meant for people who are not building software, gives answers from long documents cited to the line. Documentation, a blog and a demo booking sit alongside the products.