PageIndex
Accurate, trustworthy answers across professional documents
693 followers
Accurate, trustworthy answers across professional documents
693 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.
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Hey Product Hunt 👋
I'm Mingtian, co-founder of PageIndex.
PageIndex lets you ask across your entire document set and verify every answer down to the source line. If you work with long and professional documents where accuracy and traceability matter, financial reports, legal contracts, textbooks, research papers, etc. and you can't afford to trust an answer you haven't checked, this is for you.
✍️ Here's how it works:
Drop in your entire folder. Folder structure is preserved, and everything you add stays in your knowledge base, so you build it once instead of re-uploading the same files into every new chat.
Ask your hardest question across all of it. Ask the one you actually need: a number buried in an appendix, a clause that only makes sense with the definition twelve pages back, a figure that has to be pulled from a table and compared across four files. PageIndex goes and gets all of it.
Verify answers in one click. Every answer comes with micro-citations. Click one and the source document opens beside your chat at the right page, highlighted at the exact line the number came from.
Why you want PageIndex:
Checking a number takes one click instead of an afternoon of opening PDFs
One question runs across hundreds of documents, not one file per chat
Tables and charts are read in context
Your library compounds each quarter instead of starting from an empty chat
Leading accuracy on FinanceBench. 30K+ people use it, and the retrieval engine underneath has 35K+ GitHub stars and hit #1 on GitHub Trending.
🎉 Launch offer: code PRODUCTHUNT gets you one month of Pro free.
👉 app.pageindex.ai
Thanks for checking us out. I'll be here all day.
@mingtian_zhang I like that the folder structure stays intact. It seems especially helpful when working with a large collection of documents.
@david_turner12 Exactly! Folder structure is very important when constructing a knowledgebase.
@mingtian_zhang Congrats on the launch, Mingtian! The micro-citations are the killer feature here. Curious how it handles scanned PDFs with messy OCR?
@nicklaunches Hi Nick, thanks for your question. Scanned and image-based PDFs are supported. PageIndex runs OCR automatically, then indexes the extracted content and the document structure as usual, so you still get exact line references and can click a citation to jump straight to the source lines.
Can I share a single answer with its citations as a link, so a colleague can see the sources without an account?
@axelkane Yes! They can open the shared link and check the answer and citations directly, no sign-up needed.
@axelkane Absolutely! Just click the Share button in the top-left corner, and you’ll get a shareable link that you can send to anyone.
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.
@tmaleh_ 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!
Looks pretty scientific :) Do you collaborate with some universities and research centres? :)
@busmark_w_nika Thanks! A lot of our team comes from research backgrounds :) We care deeply about the technical side of retrieval and document reasoning, and we’re always open to collaborating with universities and research groups.
can you suggest how pageindex is better than chatgpt or claude code. what makes pageindex better?
@darcwader Hi Darshan, ChatGPT and Claude are limited by what can fit into the context window at once. PageIndex builds a structured map (aka index) of the full document set and brings the right sections into context when needed.
So the model gets focused context from the most relevant parts of the entire document set, with exact source references.
Hi PH, Ray here, co-founder and CTO of PageIndex.
I did my PhD in databases at Oxford. Years spent on indexing, and I was firmly on the side of vector databases.
I've changed my mind about them being the right infrastructure for retrieval in AI systems. I want to be precise, because the claim is not "vectors are dead".
An index is defined by the one question it can answer. A vector index answers: which chunks look most like this query?
Right question for broad recall over messy, conversational text. Vectors will keep doing that job well.
Wrong question for a long, professional document.
Two reasons it breaks down there:
The passage you need may share almost no wording with how you asked for it.
The answer often sits behind a cross-reference like "see Appendix G". No amount of similarity gets you through that pointer.
So we index the structure instead of the surface.
The document's tree sits inside the model's reasoning context.
It decides where to look next, not what looks similar.
Retrieval becomes navigation. Navigation leaves a path, which is why every answer can point back at the source line.
If you want to stress-test it: upload the longest and most complex document you own, then tell me what happens. Bug reports are worth more to me today than upvotes.