[jackienotchan]

I saw your recent $24M series A and was kind of surprised to only see you launching now, congrats!

YC seems to fund quite many document extraction companies, even within the same batch:

- Pulse (YC W24): https://www.ycombinator.com/companies/pulse-3

- OmniAI (YC W24): https://www.ycombinator.com/companies/omniai

- Extend (YC W23): https://www.ycombinator.com/companies/extend

How do you differentiate from these? And how do you see the space evolving as LLMs commoditize PDF extraction?

[adit_a]

Thanks! To clarify, we launched our document processing APIs a while ago. This launch is specifically for a new platform we're building around our API based on all of the things our customers previously had to build internally to support their use of Reducto (eval tools, monitoring etc).

Generally speaking, my view on the space is that this was crowded well before LLMs. We've met a lot of the folks that worked on things like drivers for printers to print PDFs in the 1990s, IDP players from the last few decades, and more recent cloud offerings.

The context today is clearly very different than it was in the IDP era though (human process with semi-structured content -> LLMs are going to reason over most human data), and so is the solution space (VLMs are an incredible new tool to help address the problem).

Given that I don't think it's surprising that companies inside and outside of YC have pivoted into offering document processing APIs over the past year. Generally speaking we don't see differentiation in the sense of just feature set since that'll converge over time, and instead primarily focus on accuracy, reliability, and scalability, all 3 of which have a very substantive impact from last mile improvements. I think the best testament I have to that is that the customers we've onboarded are very technical, and as a result are very thorough when choosing the right solution for them. That includes a company wide roll out at one of the 4 biggest tech companies, one of the 3 biggest trading firms, and a big set of AI product teams like Harvey, Rogo, ScaleAI etc.

At the end of the day I don't see VLM improvements as antagonistic to what we're doing. We already use them a lot for things like an agentic OCR (correcting mistakes from our traditional CV pipeline). On some level our customers aren't just choosing us for PDF->markdown, they're onboarding with us because they want to spend more of their time on the things that are downstream from having accurate data, and I expect that there'll be room for us to make that even more true as models improve.

[koakuma-chan]

Those are all ninja turtles and pdftotext is splinter.

[echelon]

How do you raise Series A before launch / PMF?

I assume y'all launched before this to select partners? Or perhaps this is a new product on top of the core product?

Congrats! Keep at it!

[adit_a]

Thank you!

To clarify, our API was already fully launched and in prod with customers when we raised our series A. This launch is specifically for the platform we're building around the API :)

[kbyatnal]

Founder of Extend (https://www.extend.ai/) here, it's a great question and thanks for the tag. There definitely are a lot of document processing companies, but it's a large market and more competition is always better for users.

In this case, the Reducto team seems to have cloned us down to the small details [1][2], which is a bit disappointing to see. But imitation is the best form of flattery I suppose! We thought deeply about how to build an ergonomic configuration experience for recursive type definitions (which is deceptively complex), and concluded that a recursive spreadsheet-like experience would be the best form factor (which we shipped over a year ago).

> "How do you see the space evolving as LLMs commoditize PDF extraction?"

Having worked with a ton of startups & F500s, we've seen that there's still a large gap for businesses in going from raw OCR outputs —> document pipelines deployed in prod for mission-critical use cases. LLMs and VLMs aren't magic, and anyone who goes in expecting 100% automation is in for a surprise.

The prompt engineering / schema definition is only the start. You still need to build and label datasets, orchestrate pipelines (classify -> split -> extract), detect uncertainty and correct with human-in-the-loop, fine-tune, and a lot more. You can certainly get close to full automation over time, but it takes time and effort — and that's where we come in. Our goal is to give AI teams all of that tooling on day 1, so they hit accuracy quickly and focus on the complex downstream post-processing of that data.

[1] https://dub.sh/ojv9b7p

[2] https://dub.sh/X7GFlDd

[adit_a]

Hey, we've never used or even attempted to use your platform. Respectfully I think you know that, and that you also know that your team has tried to get access to ours using personal gmail accounts dating back to 2024.

A schema builder with nested array fields has been part of our playground (and nearly every structured extraction solution) for a very long time and is just not something that we even view as a defining part of the platform.

[serjester]

I'm completely impartial here - seems like there's only so many ways you can design a schema builder?

[wilson090]

I've used instabase before which has had the same UX for years. What about benchmarks between the two on extraction performance?