Latent Space: The AI Engineer Podcast
00:01:17 · Jacob Lauritzen, Alessio Fanelli
[00:01:17] S0: I think we are, just at the core, probably very humble about what we're doing, because we've always been the underdog from the very, very beginning. No one believed the small startup in a basement in Stockholm was gonna, you know, outcompete this huge behemoth that was, like, way ahead of us in The US. So I think we've always been underdogs, and so we've always been very humble. We've always sort of had to fight for our existence. I think it's something we've had to learn as we've also become more and more... As we've entered The US and been in The US a lot more is to be a little bit more bold and, like, tell the world how great we are, because it doesn't come naturally to, I think, some of us. [00:01:54] S2: I kinda wanna start by going over your guys' product, because I feel like with a lot of these legal products, they're kinda like under wraps. You're not supposed But to show yeah, if you could just talk about what does the product look like, and maybe how did you guys get there? Like like, what have been the product iterations that got you to where you are today? I think that would be really interesting.
00:01:54 · Alessio Fanelli, Jacob Lauritzen
[00:01:54] S2: I kinda wanna start by going over your guys' product, because I feel like with a lot of these legal products, they're kinda like under wraps. You're not supposed But to show yeah, if you could just talk about what does the product look like, and maybe how did you guys get there? Like like, what have been the product iterations that got you to where you are today? I think that would be really interesting. [00:02:13] S0: Yeah. Absolutely. The way everything started was ChatGPT with Swedish loss Okay. Search tool in it. That was literally the entire... Everything. And so that's where we started. We started with legal research, which is like a huge market, basically, in the world where lawyers spend a lot of time figuring out what are previous cases that looked like this, what's the legal precedent, like, is this, you know, what have previous people done in these situations? That's extremely important. And so that has always been part of our product. Basically, we started from, like, an agent, you could ask questions, and it would query laws. That was a very simple beginning. And then from that, it's expanded. And so it's expanded in, like, what can the agent do? It it will now, you know, draft a lot of stuff really well. It will edit Word files. It will, you know, move your files away, organize everything, run really complex analyses, and we've added just a bunch of more features to it. So one of our features is called tabular view, which is now a name that's being used in the industry, and we're very happy to see Claude for legal use in our terminology
00:02:13 · Jacob Lauritzen, Shawn Wang
[00:02:13] S0: Yeah. Absolutely. The way everything started was ChatGPT with Swedish loss Okay. Search tool in it. That was literally the entire... Everything. And so that's where we started. We started with legal research, which is like a huge market, basically, in the world where lawyers spend a lot of time figuring out what are previous cases that looked like this, what's the legal precedent, like, is this, you know, what have previous people done in these situations? That's extremely important. And so that has always been part of our product. Basically, we started from, like, an agent, you could ask questions, and it would query laws. That was a very simple beginning. And then from that, it's expanded. And so it's expanded in, like, what can the agent do? It it will now, you know, draft a lot of stuff really well. It will edit Word files. It will, you know, move your files away, organize everything, run really complex analyses, and we've added just a bunch of more features to it. So one of our features is called tabular view, which is now a name that's being used in the industry, and we're very happy to see Claude for legal use in our terminology [00:03:12] S1: for what they're building. We about that.
00:03:12 · Shawn Wang, Jacob Lauritzen, Alessio Fanelli
[00:03:12] S1: for what they're building. We about that. [00:03:13] S0: Gotta talk about that. Which is essentially like a way to just bulk extract and bulk review many, many, many agreements, which you do in due diligence and when you raise money or whatever. So that's part of it. It's like we help lawyers just do their work much more efficiently, much, much better. And then outside of just doing the work, we're also now helping orchestrate the work a lot more than we were just even a year ago. So we have different sort of... We have a a thing called lists, which is sort of like linear for us, and we have intake, which is a way to collaborate with clients, and we have portal, which is also sort of a way to collaborate, you know, lawyer to lawyer, client to lawyer, lawyer to agent, agent to agent, maybe. We're spending a lot of time there because there's just... Frankly, the way that you work with a lawyer ten years ago or maybe even today is is really painful, and there's loads of room to do that in a much better way, to be honest. And so those are, like, I think the two things. It's it's doing the work, and it's also, like, orchestrating the work. [00:04:12] S2: When did you guys start doing or building the agent? [00:04:15] S0: So, I mean, we've all... We basically had this switch from the assistant to the agent Yeah. We call it. Right? I guess that the agent... I don't know. I wanna say a year ago, probably.
00:04:15 · Jacob Lauritzen, Shawn Wang, Alessio Fanelli
[00:04:15] S0: So, I mean, we've all... We basically had this switch from the assistant to the agent Yeah. We call it. Right? I guess that the agent... I don't know. I wanna say a year ago, probably. [00:04:25] S1: Yeah. So this is a rough timeline for people who are understanding YC twenty twenty three. Yes. I know you are you are YC founder as four. YC twenty '4. [00:04:34] S0: Yeah. I was in YC '23. Yeah. Legora was '24. Okay. [00:04:37] S1: And then... So so there is... This is like a little bit pre a... Pre, like, agents really taking up. [00:04:41] S0: Exactly. [00:04:42] S1: And then, like, using models obviously changed everything. Yes. I mean, you know, in one in one sense today, you know, we had a preview of the products. It does look... Well, preview. We had a private thing. Well, we will put up It's a video on mostly like ChatGPT for lawyers. Right? Like, like, very fair to say that. But then, like, the sidebar has a lot of nice custom tools like the tabular view, like the intake, which I really liked, and a bunch of other things. Exactly. [00:05:08] S0: That's exactly what it is. It's coworker or chat GPT, just like really, really good for legal work. [00:05:13] S1: And the for either in house corporate lawyers, people looking for accounting regulations, all these things. And we can talk about more of the the products there. [00:05:23] S2: Yeah. What models are you guys using underneath? Like, can you talk about that? [00:05:27] S0: I mean, we're using bunch of different ones depending on tasks. Right? That's Okay.
00:05:27 · Jacob Lauritzen, Alessio Fanelli, Shawn Wang
[00:05:27] S0: I mean, we're using bunch of different ones depending on tasks. Right? That's Okay. [00:05:31] S2: And then that's not available to the lawyer to see? You guys can't the agent? [00:05:36] S0: Yeah. Well, we've been back and forth on this. Yeah. Just like everyone else, I think. In the agent, there's a model selector for the primary model, but there are certain tools that we use different models in, and there's, like, tabular view, and, like, we we try... I think we found it most effective to subdivide the problem and and and route on, like, as we subdivide the task, but we do right now have a a picker in the agent. Mhmm. I'm not sure if that's gonna stay there always, to be honest. So... Yeah. [00:06:03] S1: Yeah. I mean, so I bring a little bit of context to this because I'm an adviser for Cognition. Yep. And when I joined... You know, part of my my thesis for what an agent lab is... An agent lab is, you know, ideally a domain specific GPT wrapper. Yep. Right? Like, we we we run the... We are the domain experts. We will pay attention to you. We'll we'll serve your needs way, way more than the Frontier Labs, and we'll give you the best of the Frontier [00:06:26] S0: Labs We'll have to track your development. [00:06:28] S1: Yeah. So our job is to decide for you because you're not... Your job is not to Exactly. [00:06:32] S0: We'll always keep you on the Pareto frontier [00:06:33] S2: Yes.