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PageIndex
Accurate, trustworthy answers across professional documents
682 followers
Accurate, trustworthy answers across professional documents
682 followers
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.
PageIndex
Hiii PH, I'm Cathy, GTM at PageIndex.
I'm the non-engineer on this team, which makes me test subject number one. If I can't get a verified answer out of a folder in thirty seconds, it goes back to Ray.
Before this I worked in finance. I studied it at LSE and ground through CFA Level I. None of that helps you at 11pm when you're pulling the inputs for an EBITDA build, then tracing every single number back to the page it came from.
👇 That's the bit that changed for me:
My filings sit in one folder that stays indexed.
I ask across all of them at once.
Every number in the answer carries a reference. One click lands me on the exact highlighted line, right next to the chat.
The verification pass that used to take an afternoon is now one click.
One favour if you're testing it: please don't ask what Apple earned last year. Any chatbot answers that. Ask the thing only you would know where to look for:
a covenant threshold buried in an appendix
a segment number that moved between restatements
a figure that only appears in a footnote
If anything confuses you in the first two minutes, tell me bluntly. That's the feedback I act on fastest. I'm in the comments all day ☕
what is the latency for this kind of search?
PageIndex
@yilu 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.
Teable
Feels like what Perplexity does for the web, but pointed at my own document library instead.Great Launch!
PageIndex
@jocky Thanks Jocky! That’s exactly the idea: deep research for your own documents. Been following Teable for a while too 🙌
PageIndex
@jocky That’s a great way to put it, Jocky! Perplexity for your own document library is actually pretty close to the experience we’re aiming for — with the added focus on traceable reasoning and exact citations back to the source. Appreciate the support!
Love how easy it is to verify an answer without leaving the chat.
PageIndex
@shirleyw Thank you! That's exactly what we wanted: not just citations, but a quick way to verify the answer by jumping to the exact source lines.
Acti
PageIndex
@yura_acti 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!
Ada.im
This seems especially valuable for investors and researchers.
PageIndex
@s_cen 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.
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.
PageIndex