PageIndex - Accurate, trustworthy answers across professional documents

PageIndex gives you accurate, trustworthy answers across long, professional documents your work depends on. Bring in your entire document set, ask your hardest question, and click any citation to jump to the exact highlighted source line, so you can verify it in seconds.

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The document should always be the source of truth. I like that AI is supporting the reading process instead of replacing it.

 Exactly. That’s the philosophy behind PageIndex — AI should help you navigate and understand the document, while the original source stays one click away for verification. Thanks for calling this out!

Great use case at the core of the project! I often check contracts with ChatGPT the same way when I need to quickly find payment terms or deadlines.

Exactly, contracts are long and technical, so ChatGPT can sometimes miss or invent important details. PageIndex gives you accurate, reliable answers, with the exact source line highlighted in the original contract for instant verification.

This seems especially valuable for investors and researchers.

 Absolutely, those are two of the core use cases we had, especially when you’re working across long reports and filings and need to verify everything back to the source line.

Super useful tool. Congrats Mingtian and team. Does PageIndex tell me which version it pulled from? Data rooms I read usually have the same number living in three versions of the same file - original, amended, restated.

 Of course, Taissa! For every number PageIndex gives you, you can see exactly which document and version it came from, then jump straight to the cited source line. You’re more than welcome to try PageIndex on your data room!

what is the latency for this kind of search?

 Thanks for asking, Yi. In PageIndex's approach, the documents need to be pre-processed once to generate a tree structure, which is slower than the vector-based method. However, during retrieval, this approach only requires the LLM to condition on the context and does not require an embedding model to convert the query into vectors. As a result, it can be efficient when the tree is small. When the tree is large, it may be slower than the vector-based method, since it prioritizes accuracy. For documents where a wrong number costs a lot more than a few extra seconds, that's the trade we picked.

Love that I don't have to keep scrolling through a 500-page PDF anymore!

 Haha yes, that’s one of the best parts 😄

Interesting. Curious how is this different than SciSpace, SciSummary, Jenni, Elicit, etc?
Hi Erkang, great question! Those tools are primarily built for academic literature discovery, summarization, or writing. PageIndex is designed to reason across your own long, complex documents—contracts, financial filings, technical reports, as well as research papers. Every answer is grounded in citations you can click to verify against the exact source lines.

the one click jump back to the exact source line is the killer peice 🙌 nice work

Looks like a life changer. How well can it handle unusual documents like textbooks is going to be nice to see. 👋😄

How different is this from Rag (gemini llm)?