PageIndex - Accurate, trustworthy answers across professional documents

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:

1. 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.

2. 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.

3. 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.

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 ☕

Feels like what Perplexity does for the web, but pointed at my own document library instead.Great Launch!

Can I share a single answer with its citations as a link, so a colleague can see the sources without an account?

I like the exact-line citations. Congrats!

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

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.

Congrats! Avoiding embeddings is an ambitious architectural choice. I'm especially curious whether this makes the results more interpretable, since each search decision could potentially be traced through the document tree.