00:13:41 · Alex Immerman, George Sivulka
[00:13:41] S1: how it's thinking. [00:13:42] S0: You could start to change the cells. You could start to act... It's almost like a sensitivity table. And so you're not only kind of telling the AI what to do and seeing its output, but you're working alongside it or co working and collaborating with it. And right now, the limiting factor for much of AI, especially what we do in financial services, legal, knowledge work more broadly, is information retrieval. And so it's very document centric. But we're already exploring and have built a multitude of interfaces where you can see inside the AI's mind doing different things. And that is a human first approach. It's human and AI kind of fusion together. So, as [00:14:19] S1: you think about the new capabilities, there's computer use, there's reasoning. Think those are really instrumental in being able to drive more of these agentic workflows that you're referring to. How are you thinking about leveraging computer use and heaviest product?
00:14:19 · Alex Immerman, George Sivulka
[00:14:19] S1: you think about the new capabilities, there's computer use, there's reasoning. Think those are really instrumental in being able to drive more of these agentic workflows that you're referring to. How are you thinking about leveraging computer use and heaviest product? [00:14:35] S0: I think that the way we've designed our product is not to be just the interface to AI, but actually to be a tool that we would hope AI or AGI would choose to use. And so that's a bit of a meta statement actually. It's saying, well, rather than try to build the AGI, how about we build the AI platform that is so good that if AGI were to complete a task, it would choose to use Hebia to do that task. And to that end, I think computer use and a variety of these other tool use phenomena from the large language model providers are just creating universal APIs to other tools. Products like Instacart might still be the best way to deliver groceries, right? If you're AGI. Right? You don't want the AGI to go build a robot and go get it in itself. And products like Hebio would be probably the best way to do structured and unstructured data processing, the knowledge work tasks with sub AIs. You wouldn't want an AGI to jam a 100,000 documents into its context window and take an infinite amount of time to, or a very large amount of time to process that. You'd rather use and orchestrate a bunch of sub agents. And that's a little bit of the philosophy behind what we do.
00:15:44 · Alex Immerman
[00:15:44] S1: And as you think about building for this world with financial services, a lot of what you just talked about is more around the infrastructure, the architecture of what you've built. There's also the interface layer, the application layer, the interfaces, the data that you're leveraging, the integrations, the workflows. Maybe, like, go a layer deeper there. What are you specifically doing to make the, whether it's an AI agent, or today, a human that's in the loop, most effective as a financial analyst?
00:16:15 · George Sivulka
[00:16:15] S0: One of the big pieces and unlocks that had the experience with was coming to the realization that nobody knows what to use AI for. People pretend to go and create all these amazing demos, but nobody knows what to do with it. And you can actually look at our product today, and you could argue that it could do almost any task that a junior analyst could do better than a junior analyst. But even knowing what tasks it should do becomes a problem. It's actually no longer a technology problem. It's really a sociology and change management problem. So there's an integrations piece. You know, there's a process piece of making sure it works for specific workflows. But the real differentiator is actually knowing what to use it for, knowing what is best in class, and helping evolve the firms and do the change management required to create an AI centric workforce. We're starting to see the first year or second year out of college, like analysts come out and be like Hebia native, where they, instead of doing it and reading every file by hand and then putting things into an Excel or making different documents, they're actually starting to use AI and incorporate it into their workflows, and
00:17:20 · Alex Immerman
[00:17:20] S1: it's making them like that much better. That all makes a ton of sense. For most AI applications, there's two vectors of the value proposition. There's one, faster speed to get the same result. So what used to take four hours, now it takes four minutes. And then there's the second vector, which is net new results. Things that maybe with infinite workers, with infinite memory, you could figure out, but those are like the magical experience moments. What the common use cases you're seeing today?
00:17:53 · George Sivulka, Alex Immerman
[00:17:53] S0: You're right to put it into two buckets. There's the time savings piece, and then there's like the stuff we wouldn't have done if we didn't have AI. And I'm way more excited about the second piece, you know, I think everyone is, versus here, you know, your analyst is 80 times faster. Like, on the buy side, we work with a lot of the largest asset managers in the world. And a very common use case is they get a gnarly data room. It's got 40,000, maybe a 100,000 files in it. They put it in heavy and it automatically builds out a variety of different analyses without having to do anything, without having to even open your computer. You have, you know, a lot of the different things you're looking for, customer concentration. You have an understanding of all of their expert network calls. You know, it actually does the analyses for you. That's a time saver. Mean, saves probably twenty to thirty hours on a deal process, depending on how disgusting these VDRs can get. But there's an even more beautiful piece of the puzzle, which is the net new piece. On the net new side, I think a lot the reason people become investors or work in finance is because they want to be discovering. They want to be doing the act of almost like a Sherlock Holmes looking through the data, looking through what the public markets are saying. [00:19:00] S1: You're constantly learning. You're constantly finding something new. That's what... That's like part of where you get your energy.