The Missing Infrastructure for AI Agents: Documents | Reducto’s Adit Abraham

Adit Abraham (0:00)

The day we got into YC, we told that we were going to pivot. Nobody thinks about complexity when they upload a document to ChatGPT. You just expect it to work. On the other hand, the tools that you had to solve them were much, much more powerful. Documents to us are almost these abstractions and artifacts of human work. It's the first time where I find that agents can be better at structured extraction than humans on many tests. The best version of Reducto is one where you can kind of abstract away the PDF processing problems, even if the PDFs contain these sub intricacies along the way.

Jennifer Li (0:29)

Even the big model labs are also focused on knowledge work. Why do you think this is not a problem that they will solve where it requires independent companies like Reducto to really focus on it?

Adit Abraham (0:40)

One of the things that's underappreciated is how deep the range of problems gets. Yes, the labs could maybe go into this deep dive of trying to build all these individual tools, but I personally believe that they're on an arms race for...

SPEAKER_3 (0:52)

AI agents are getting dramatically better at reasoning, but much of the information they need to do real work is still trapped inside PDFs, spreadsheets, scans, and other documents that machines struggle to reliably understand. In this episode, a16z General Partner Jennifer Li sits down with Reducto co-founder and CEO Adit Abraham to unpack the surprisingly difficult infrastructure problem behind getting AI agents to work with documents. They discuss why traditional OCR isn't enough, how vision language models change document processing, and why a model that looks accurate on a benchmark can still silently drop critical information when it encounters a long or messy document. Adit also explains how Reducto built its own specialized model, why agents can sometimes outperform humans at structured extraction by reviewing and correcting their own work, and what becomes possible when AI can not only read documents, but reliably edit and act on them. Because as agents move into health care, legal, finance, and insurance, a document processing mistake is no longer just an OCR error.

It can change a real-world decision.

Jennifer Li (2:02)

Adit, welcome to our podcast studio. It's so great to have you here.

Adit Abraham (2:06)

Thanks for having me.

Jennifer Li (2:07)

I would love to start a conversation by asking, when you and Ronick first started working on Reducto, I think working on some memory-related problems, what turned the attention to documents and why it's still not a solved problem?

Adit Abraham (2:20)

So we started in 2023 when models had horrible context windows, and so memory was this top-of-mind thing that you could feel. And I remember the day we got into YC, we told Diana that we were going to pivot, which she was surprisingly accommodating about.

The framing retroactively that we have that, honestly, I think wasn't actually as lucid back then, is when you think about it, agents are going to do all sorts of human work, and it happens to be the case that documents to us are almost abstractions and artifacts of human work. So much white-collar work across healthcare and legal and finance and insurance is in things like PDFs and spreadsheets. You read from them, you write to them, and they catalog that knowledge.

So if you're going to have agents doing work in healthcare reading things like patient records, it's really important that you have the same fidelity that you did when a human was looking at it. It's not like an oops to say, oh, I messed up a checkbox. It's like, oh, that's a patient's outcome. Right. And so that's where we got really excited of thinking of it as you're going to process more documents than ever before in history. How do you build the sort of interface that connects synthetic intelligence and human knowledge? That's been a really deep spiral. The honest answer of how we actually got here is some amounts of right place, right time, like vertical AI apps were first spinning up. And our background was in computer vision. And we started talking to a lot of these early AI native companies and surprisingly kept finding this recurring trend of people were struggling with their document processing infra. And because our background was in CD research, we wanted to look at it from this broader problem of how would you read it the way that a human would have? And it's spiraled from there.

Jennifer Li (3:58)

I guess maybe break it down a little bit. There has been OCR technology from, I think, 20 years ago.

Adit Abraham (4:04)

A while.

Jennifer Li (4:04)