Building Legal Agents When You Can't See the Data - Jacob Lauritzen, CTO of Legora

People were asking us about Mythos and everything, even though I think realistically a lot of legal work does not require Mythos-level intelligence. Paralegals, gone. Yeah. Junior lawyers, gone. I don't need them. [laughs] Um, that's actually not what we're seeing. And we had one problem where, like, it wouldn't cite properly, and so we were like, "Next model release solved."

It went from, like, [laughs] seventy percent to, like, ninety-nine. It's just like we did nothing and, like, great. But if you're doing a, you know, four hundred million dollar M&A transaction, you're gonna want Lagora. We're just gonna be twenty engineers for the rest of the company's history, and, like, we're gonna beat everyone who's much bigger. Uh, we're a hundred engineers now, and it feels like we're way too small.

But it's something that I'm toying with is, like, how much of our product can just be the agent versus all the other stuff? [upbeat music] Okay, we're here in the studio with special guest, uh, Jacob Lauridsen, CTO of Lagora. Did I get it right, Lauridsen? Lauridsen. That's fine. Yeah, that's okay. And that's Danish.

I'm not super sure that how... It's Danish, yeah, exactly. Does it have a special history behind it? It's just, no, sen is like son, son of. Son of Laurits. So just like you have son and... Yeah. [laughs] So son of Laurits, that's my name. [laughs] Nice. Uh, we've crossed paths here and there. You, you, you spoke at, uh, AI a few times, and obviously Lagora is one of the fastest-growing legal tech in history.

Yep. And, uh, y- you said something at the start in our pre-show that I thought was really interesting. You said that your origin is very European. Mm-hmm. Uh, even though obviously you're expanding in New York and SF, what feels different about your culture? I think we are just at the core probably very, uh, humble about what we're doing because we've always been the underdog from the very beginning.

No one believed the smallest startup in a basement in Stockholm was gonna, you know, out-compete this huge behemoth that was, like, way, yeah, ahead of us in the US. So I think we've always been underdogs, and so we've always been very humble. Um, we've always sort of had to fight for our, uh, existence.

Um, I think something we've had to learn as we've also become more and more, um... as we've entered the US and been in the US a lot more, is to be a little bit more, uh, bold and, like, s-s-tell the world how great we are 'cause it, it doesn't come naturally to, I think, some of us. I kinda wanna start by going over your guys' product, 'cause I feel like with a lot of these, like, legal products, you know, they're kinda, like, under wraps. Like, you know- Yeah, sure. [laughs]...

you're not supposed to, like, show it. But, um, yeah, if you could just talk about, like, what does the product look like and maybe, like, how did you guys get there? Like, like, what have been the product iterations that got you- Hmm... to where you are today? I think that would be really interesting. Yeah, absolutely.

The way everything started was ChatGPT with, uh, Swedish laws- Okay... and 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, um, lawyers spend a lot of time figuring out what are previous cases that look like this? What's the legal precedent?

Like, is this... You know, what have previous people done in these situations? That's extremely important. Um, and so that has always been part of our product. Basically, we started from, like, an, an agent you could ask questions, then it would query laws. That was the very simple beginning. Um, and then from that, it's expanded, and so it's expanded in, like, what can the agent do? It, it will now, uh, you know, draft a lot of stuff really well. It will edit Word files.

It will, you know, move your files away, organize everything, um, run really complex analyses. Um, and we've added just a bunch more features to it. So one of our features is called Tabular Review, which is, um, now a name that's being used in industry, [laughs] and we're very happy to see Claude for legal using our terminology- [laughs]... for what they're building.

[laughs] Ooh, we gotta talk about that. We gotta talk about that. Which is essentially, like, a way to just bulk extract and bulk review many, many, many, um, 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, um, 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 s-sort of like Linear for us. And, um, we have Intake, which is a way to collaborate with, um, clients.

And we have, um, 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. [laughs] We're, we're spending a lot of time there 'cause, um, 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- [laughs]...

in a much better way, to be honest. [laughs] 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. When did you guys start doing or building the agent? So I mean, we've al-- We basically had to switch from the assistant to the agent- Yeah... we call it, right? Um, I guess that the agent, I don't know, I wanna say a year ago probably. Yeah.

Um, something like- So this is a rough timeline for people who are understanding. YC twenty twenty-three. Yes. Uh, and now you were, you were a YC founder as well. YC twenty-four. YC twenty-four. Yeah, I was in YC twenty-three. Yeah. Lagora was twenty-four. Okay. And then, uh, so, so there is-- this is, like, a little bit pre a- pre, like, agents really taking off. Exactly, yeah.

And then, like, using models obviously changed everything. Yes. I mean, you know, in one, in one sense today, uh, you know, we had a preview of the products. Uh, it does look... Well, preview. We had a private, uh, thing. But we will put up a video on the, on the YouTube. Private screening, yeah. It's mostly like ChatGPT for lawyers. Yeah. Right? Like a very- Yeah...

like, very fair to say that, but then, like, uh, the sidebar has a lot of nice custom tools like the Tabular View, uh, like the in-intake, which I really liked, uh, and, uh, you know, a bunch of other- Yeah... things. Exactly. Yeah, that's, that's exactly what it is. It's, it's CoWork or ChatGPT- Yeah... just like really, really good for legal work.

And, yeah, the, the, the workflow for, like, uh, ei- either, like, in-house corporate lawyers, like, people, like, looking for upcoming regulations- Yeah... all these things. And we can talk about m-more of the, the products there. Yeah. What models are you guys using underneath? Like, can you talk about that? I mean, we're using a, a bunch of different ones- Yeah...

depending on tasks, right? That's, um- Okay. And then that's not available to the lawyer to see. You guys pick- For the agent-... yourselves. Yeah. Well, we've been back and forth on this. Yeah. [laughs] Yeah. [laughs] To be-- 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 Mm-hmm. I'm not sure if that's gonna stay there always, to be honest. Ah. Um, so yeah. Yeah, I mean, so I bring a little bit of context to this because I'm advisor for cognition. Yeah.

Uh, and when I joined, you know, part of my, my thesis for what an agent lab is, an agent lab is a, you know, uh, ideally a domain-specific GPT wrapper. Yeah. Right? Like we- Yeah... we, we run the, uh... We are the domain experts. We'll 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 labs- Yeah, we'll abstract away all the-...

according to our judgment. Yeah. So our job is to decide for you because your not... your job is not to- Exactly. We'll always keep you on the Pareto frontier- Yes... and make sure that it just is great all the time, and you don't have to dive into all the details. 'Cause like, one of the things that I've...

Exactly to this point, if you go to a lawyer and you ask them, "Oh, you're gonna, you know, uh, draft this contract based on this precedent, do you think that's a Sonnet high or an Opus mid task?" [laughs] You know, they, they won't know. Just give me the smartest model- I know exactly. Just, like, make sure it works... my hourly rate is $1,000 an hour. Exactly. Just like- Make it work [laughs] Um, so yes, I originally came in with that. I was like, "Oh yeah, this is, this is what Devin does differently." And now we have five things in the model picker, including Fable and Opus [laughs] and, and- Yep... Astra.

Yes. And, uh, people want to experiment with the fancy new model. Yeah. Yeah. Uh, do you- That's the- Is that the same? I, I think so. I think it's prob- It's, it's definitely way less in legal than it is in, um- Coding... engineering. Yeah. But there are definitely people that just want Fable 1 right away, and you know, people were asking us about Mythos and everything. Yeah, yeah.

Um, even though I think realistically a lot of legal work does not require Mythos-level intelligence. Yeah. There is- Yeah. So, so exactly, right? Yeah. Like, I think basically y- you're like, "Okay, we'll give you the option. You, you ask for this- Exactly... but you're gonna run down your credits, like, 10X faster than them." [laughs] Yeah. I would have loved...

We, we fought introducing it for a very long time, and then in the end we, we just kind of caved in, and we're like, "Okay, if you want this level of control- Yes... you know, then we'll give it to you." Yeah. So you think the models are already, like, as good as lawyers need? Like, there's no need for them to get better? It's a... I think it's a power distribution. So like, there's probably like 60% of the work- Yeah...

we can do really, uh, well right now, and the... You know, from now on, it's like a cost and latency thing. But then there's the rest of the, the thing where especially for, like, very complex analysis, we, we still need more intelligence. And then I think there's also just the work expanse kind of. So like, that's the work that we have right now, but also the work for the lawyer who thus probably expands with intelligence- Yeah... I imagine.

Early on, like, when you guys first started, I guess, transitioning to the agents- Mm... or building the agents, like, what were... Like, what models were you guys using? I guess it would've been, like, Opus 4.6 or something. That would've been... Yeah, Sonnet 4.6, Opus 4.6 is probably right. What were the agents, like, not good at at that point- Mm...

that they're much better at now, even though it's only a li- like- Yeah. Yeah... nine, nine months later? Subagent delegation was one of them that we've gotten working really well now for, like, fan-out type retrieval queries. Yeah. Works extremely well for us now. Um, that was difficult before. We basically track...

So we have a team that's just doing evals, and so we track all of the models' performance in different practice areas, which is, like, subgroups of, of legal, and different tasks across all of those. So we basically have, like, a big heat map, and we're like- Hmm. And then we can see them move, and it's like usually they improve most stuff, but there's actually a lot of model releases where they improve some and, and regress in others. Okay.

And that actually happens a lot more than you'd think. Like, what are some of those tasks? Like, are you familiar with how you guys are running a lot of the evals? Mm. Yeah. Like, like what are those different subsegments, I guess, that you look at? Yeah. So like, there's, um, there's different just, like, framing stuff.

So we have different basically, like, worlds, like RL environments essentially, which is like, "Here's a..." 'Cause, 'cause lawyers work in projects always, more or less. So it's like, "Here's 10,000-file data room. You're doing a due diligence. In the end, a great report looks like this. Like, it contains all these ri- red flags, and, like, it doesn't contain whatever weird, uh, stuff, and, like, we want it to be concise," et cetera, et cetera. That's kind of how we've built it.

So there's different tasks, which is like, part of it is drafting a report. Like, does the draft look good? Does it contain all the right stuff? Is the wording sound, the analysis sound? There's also the part of finding the red flags, like the analysis before that. So we can s- kind of like subdivide it a little bit. And th- and that's how we run the evals. Interesting.

Like, what does your evals team look like? Like, do you hire lawyers to, like, do that? So yes, it's a mix. We have, uh, engineers, and then we have lawyers, and it's one team. So there's, like, three engineers and, like, three lawyers right now, and then we have a pool of what we call legal engineers, which is, um, people that have worked in Big Law for, like, three or four years.

Typically, maybe they have a CS degree, and, like, they're very tech forward, and then they come to us and because they wanna, like, help change, uh, the industry. Are they IT in Big Law, or- No... are they lawyers? They're lawyers. Lawyers in Big Law. They're lawyers in Big Law. With CS degrees. [laughs] Yes. Oh my God. Must be really hard to find those. It's a Venn diagram that... Yeah, exactly.

[laughs] That's, like, our hardest person to hire for. [laughs] But they're also such a force multiplier 'cause we can pull on them for the eval stuff and for the product development, which is like you have a pool of 100 experts that use the product every day and go and talk to clients every day, and also, like, they live this and have lived this, and we just pull on them to do evals and to, like, help us shape the product, and it is such a, such an advantage.

And curious, like, when did you guys start that team? Evals, um, grew out of the assistant agent team at some point. I think there was a point up until recently where it was like, honestly, just not really worth it 'cause, like, all models just got better. So it was like, do we wanna spend a lot of our time doing really complex evals, or do we just, like, wanna build...

'Cause there was so much to be gained from building a better harness and, like, just sort of leaning into everything's gonna improve, and everything kind of just improved. Hmm. Yeah, like every two, three months you get just a frontier model. It just like solved. I'm-- Yeah, exactly. I'm- Just pass it through, right? Exactly. Yeah.

I mean, we had one problem where, like, it wouldn't cite properly, and so it would be difficult to follow, and it was like next model release solved. It went from like [laughs] 70% to, like, 99%. It's just like we did nothing and, like, great. Yeah. Maybe the, the, the real work is, uh, you know, keeping all the data private, making it- Yeah...

taggable, sortable, ad- adding new features like the, the tabular view. Yep. Yep. Maybe is that the, you know, may... Like- You mean over and above ChatGPT? Well, I mean, like, yes. But obviously now you're also doing evals. Sure. I assume your, your use of open models is also increasing. Yes. Yeah. We can talk about that. Uh, so yeah.

I, I don't know what I'm saying here apart from like, you know, what is the job? The job is- Yeah... uh, maybe not... Uh, in the early days, it was not so much to do, do evals- Mm... because it was mostly exposed model capability- Yeah... to lawyers. Yeah. The huge part of it is just, um, figuring out what does the legal profession look like in five years, 10 years with all of this stuff.

Like, coding is maybe a year or two ahead, so like we can kinda look to that a little bit, but like what are the primitives? How do people work? How do you collaborate if it's not a 200-thread email with like V5 Word files back and forth? Like, what does that look like? And that's like not even necessarily purely AI.

That's just like build [chuckles] a much better product experience- Yeah... and make sure you do it with like audit logs and data retention and all of those sort of really important things in regulated industry. And then there's the, the AI part, which is like make the work faster and better and, and proactive agents that react to stuff when it comes in so you don't have to, you know, uh, kick off everything.

So I think a lot of the work is just figuring out what do we think is gonna look like in five years. If, you know, model, um, capabilities keep increasing, if price keeps coming down, what could we do? Can you log in and it's like command center where it's just like you need to react to these five things that the agents have been doing lately, and like review and check and improve the system.

Maybe that's where we're going, or is where we're going. Yeah, yeah. Yeah, I was gonna ask, like could you go into that in a little more depth? Like, yeah, what, what is the like law profession gonna look like in a few years? [laughing] [laughs] I should preface this by saying I don't know. Yeah. I mean, who knows really?

But um, I think we can look to coding a lot. I think, um, all lawyers are gonna be way more, uh, productive than they were before. They're gonna be able to do a lot more stuff at a higher quality, uh, for, uh, lower cost. I think the profession itself, the work itself, is gonna improve a lot. It's gonna be a lot more...

I think lawyers are gonna spend more of their time doing the stuff that they became lawyers to do, uh, you know, figuring out what's the right risk profile, what's manageable risk, what's like the... all of their judgment, strategic judgment and like working with clients. They're gonna do that a lot more, and much less of the like trolling through big documents to like find the smoking gun or like do all this menial, super annoying work that a lot of lawyers have been doing.

What do you think is gonna happen in the partnership model? Like, do you think that stays or like what happens? I gave this... I actually gave this a lot of thought just like last week. Yeah. 'Cause the, the partnership model-- I don't wanna get i-into too much of the details, but like the partnership model kind of exists because the only type of capital that these companies have is human capital.

So like, it's not like, you know, us or like a software engineering firm or like a manufacturer, whatever, you h- you have physical assets or like some sort of intangible assets that, that you accrue over time and, and they pay dividends to the shareholders. But if you work in an industry like professional services, and it's the same for like, uh, consultancies and, and whatever else, all of your value is in your people, and you can't really own human capital.

You know, you, you can't [chuckles] you know, slavery's abolished. You can't own people. But you can rent them. You can pay them money. And so partnerships essentially is a structure where you make the human capital the owners of the company so that they stay. Yeah. Will that change? Like, I don't...

It doesn't have to, but I think what we really wanna do with the lawyers, we actually want lawyers to be able-- law firms to be able to build some sort of intangible capital, some durable capital, and like you can build a system that learns of how you work. A lot of firms right now think about like, well, there's the X law firm way of doing things, and we can codify that, I think, and we can build a system.

And whether that's like a self-improving knowledge graph or whatever it is, and like precedent files and documents where there's like a way of working that produces a really good out- output, and that can be their IP. So it's like a way to codify the human expertise and judgment into a system. That, I think, is... Now I'm just rambling completely, but like, um, that's something that we want to help empower. Mm.

And does that change the partnership model then? 'Cause the incentives are different in the partnership model. You just gotta bring more clients, and you don't wanna take away time from bringing in clients to codify your information, basically. Mm. 'Cause like you get rewarded from, you know, eat what you kill or- Yeah...

make this luck step, whatever. Um, that needs to change a little. We need to figure out an incentive structure where partnerships-- uh, partners are incentivized to codify things for-- to build an asset for the company. But right now they aren't, 'cause as soon as they leave the partnership, they don't, um, get any dividends from the assets that they built. Yeah. So there's like a... Yeah.

I think the, the, the whole business model is, is, uh... We'll, we'll see. [laughing] Yeah, yeah. It's all, it's all in question right now. Yeah. My, my actual other take... You, you know, so on the partnership level, that's like the top end of the- Mm... the industry. Actually, my take is more on the bottom end, right? Yeah. What happened there? Paralegals, gone. Yeah. Right?

Like, junior lawyers, gone. I don't need them. Like it's- Yeah. Um- [laughs] That's actually not what we're seeing. Okay. So yeah, there's a... I think it's... I don't know. Would you, would you say that we're seeing that in engineering? Uh, yeah. Yeah. So, uh, junior engineers are- Really kind of cooked... kind of cooked. Yeah.

Like you kinda have to come out of-- uh, drop out of university fully baked or else you're [laughing] Or you're not gonna make it. [laughing] [laughs] Um, I feel like I'm, I'm being a bit dramatic for, for, uh, comedic purpose. But like, uh, yeah, I know a lot of lawyers, they started as paralegal. They go through law school, all these things, right? Yeah. And now they don't-- they're not needed. Yeah. They're seriously just not needed. Yeah, yeah. Yeah.

It, it, it's interesting what happens to the partnership model, like the pyramid. I think generally a lot of lawyers, um... You still need ju- you need the next generation. And at least for engineering, the way I think about it is you have a good engineer today, they're worth a lot more even than they were two years ago 'cause they can produce so much more stuff, like if they're good.

And I think it's the same thing in legal, where like they can actually do a lot of really good work if they're good. So I think it's more that like really good lawyers or junior lawyers are worth a lot more. Yeah. It's- And- It's always been the case, right? Yeah. Yeah. Exactly. But it's even more so. But it's like, it's, yeah, it's polarizing even more so. Yeah. I'm actually curious about that.

So like the whole like law business is basically like, um, charging per hour. Mm. So- Yeah... you know, if each project now can be done- Is faster... a lot faster- Yeah... is it going to be like way cheaper? Like- This was actually- Yeah... so, um, fun story. I was, I was reading Max's YC application, uh, and like helping him getting into YC, like Leia as it was called back then in YC. And this was my one thing.

I was like- And, and curious, Leia is a Danish thing for something? No, uh, Swedish. Swedish. Um, no. Also, it means nothing. [laughing] [laughs] Okay, okay. There's this really funny story and, um- I think it was a Spanish newspaper that wrote about us in the early days, Leia, and then they, they explained that the abbreviation was Legally Efficient Yoda Assistant.

[laughs] Why Yoda? Yeah, I don't know. [laughs] I'm just like, "Why, why is it-" See, hallucinations are not just from LLMs. No, I think journalists hallucinate. [laughs] Um, but no, it, it doesn't mean anything. Um- Uh, so he wrote the application. He, he wr- And my feedback to him was, um, you know, people have tried this a million times. Legal tech has never been a thing 'cause, like, there's no incentive for lawyers to, to...

Why would they be more efficient? They earn less money. Like, it doesn't make any sense. But I think the reason that lawyers do wanna adopt this is that they understand the gravity of the situation. They know that, like, the business model and everything is under pressure, and it's like they need to change 'cause the competitive pressure's so high. The billing model itself I think is also gonna change.

We've- we're already seeing that, particularly in the US, that a bunch of stuff is actually being priced on, like, a project fee and not on an hours fee. Um, particularly the, like, sort of lower-end stuff on the spectrum, like, whatever it is, like, small reviews and whatever. Some of it honestly is being done for free. Mm.

And some of it is just being priced on a flat rate. So where is the money being made, then? Is it... 'Cause I, I- In the strategic work. Yeah, I feel like a l- lot of what you guys talk about is M&A specifically. Like, that's where all the money is made? That's just a, a very easy example. Oh, okay, okay. Yeah, yeah. Litigation's also...

The big work still is there, and it's very valuable, and so they'll, they'll spend-- lawyers will spend more of their time there. Client relationships are also super important. Like a CRM? No, just, like, spending more time with the client. Oh. Like, a lot of law firms will, or historically have basically done stuff for free just to have the relationship with the client. Yeah, yeah.

Uh, and so now they can do that, but they don't have to lose a lot of money on doing it. [laughs] Wait, I thought, I thought I just have to pay s- the lawyer money just to be my friend. Like, this- Yeah. [laughs] Like, isn't that the retainer thing? Yeah. [laughs] Yeah. Yeah, that, that, that's, that's- You, you, you tell me I can do it for free? Like... Yeah, you just have to find the right lawyers. Oh, goddammit.

[laughs] Yeah. [laughs] Uh, okay, cool. Or the legal space has to grow much bigger, I don't know. Yeah, yeah, we're trying. We're trying. [laughs] Uh, you know. Yeah, so okay, so, so, uh, uh, so you're saying, um, you know, obviously the, the strategic work and all this high-value add is still there. Mm. But our legal fees are not going down.

But you also said paralegals are not affected- Mm... or, like, uh, they're, they're just shifting. And, and this is something that, uh, we also did, we, uh, worry about with Cognition. Yeah. Like, uh, people worry about the phrasing of, like, "We will replace your junior engineer"- Mm... 'cause nobody likes that kind of statement. But i-a-in actual fact, yes- Yeah...

like, some, some part of it, uh, does happen. Other junior engineers embrace it, and they become more productive- Exactly... exactly like you just said. Yeah. So I guess, like, i-is, is there a new term for this role or this, this job that is tak- that is having traction with your customers? You mean the, the super AI built junior legal? Nothing I've found yet.

Maybe we should coin one. Isn't it legal, legal engineer? Legal engineer? That's our, and, and also now an industry thing. Yeah. Yeah. CSJDs. Exactly. [laughs] Yeah, yeah, yeah. Yeah. [laughs] And maybe that's, they're those are the people that are, uh, best positioned to do this kind of stuff. I think the, the, the derogatory term meat proxy- Yeah...

where you just, like, sit in front of the machine and type in something that- Yes... someone else tells you to type, and then you repeat what the machine tells you to say. Yep. But, like, I do think a very good meat proxy is just, like, was you know agents very well, and you, you know Legora very well. Yeah. And you know how to, like...

Like, this is my toolkit, and, you know, every industry has a toolkit. Yeah. And I can use my tools to do the thing that you need me to do. But I, I guess then you're not really a proxy, right? You're actually just doing meaningful work, I suppose. Yeah. It's, it's just, like, I no longer... Like, you know, uh, well, I'll talk about Tabular Review 'cause that- Sure...

that's, that's the thing that happens a lot. I have hundreds, thousands, millions- Mm... thou-thousands. We do up to a million cells, I think. In a, in a si-single day? In a... Yeah. That's impressive. Yeah. Uh, we should talk about the, the- Yes. Yeah... the, the table and the windowing. Yeah, these, you know, like, I've, like, I'm working on a big case. I have a million documents. I type it in.

Usually, I'll throw a par- throw, like, a team of paralegals- Yes... locked in a room. And they'll sit there till, like, 4:00 AM. [laughs] Yeah. Like, that, that doesn't need to happen, right? Exactly. 'Cause now they can extract it, and you can go and re-prompt everything. Mm-hmm. So, like, yeah, I, I think now I don't need a paralegal for that, but, like, a, a, a good, like, super AI-pulled paralegal would do something else, you know? Yes, yeah, exactly. Whatever that is. Yeah, yeah.

They can do more stuff- Yeah... or, like, spend more time on the things that they, you know, didn't have time for before. Or actually sleep. Who knows? No. [laughs] I have a question. So you guys bought a company, Kira. Yes. Is, is that how you say it? Yeah. Yeah. And you, you guys used your own product to do the M&A? Yes, we do that all the time. So how did that work? Ooh. Like, can you talk a little bit about that? Dogfooding. Yeah.

No, that works great. We're doing that a lot. Our legal team is, is... I mean, intake has been built with our legal team. Yeah. 'Cause right now we have a Slack workflow where everyone flows their stuff in, and they're like, "This is annoying." Yeah, yeah. Right? And inefficient, so it's like, great. We should use Legora for that. Yeah, yeah.

Uh, can you talk a little bit about that? Like, you know, what that process looked like? Yeah, absolutely. So it's, it's, um, we, uh, do a lot of the due diligence ourselves because we can do it, uh, with Tabular Review and other tools. We do still have counsel coming in- Mm... um, to, like, assess certain risk or, like, you know, there's payroll.

And so Kira was simple 'cause that was all in Sweden, but, you know, we've done one in Canada and one in Australia, and so there's, like, payroll in Australia. Okay, do we want someone to look at this, like, a bit more strategically? So there we will still use lawyers for the strategic part, where they can actually be sparring partners and help us a lot on, like, how to figure out how to structure this the right way.

But a lot of the more menial stuff we could actually just do ourselves. Mm. So, like, a lot of, uh, yeah, a lot of the early things we would find... And we have had cases where we found something, uh, [laughs] that's been, like, a red flag, and then we've, we've pulled the brakes. Can you talk a little bit about any of those? No, I don't think so. [laughs] Yeah.

All right. Early on you said you, your early customers were Swedish- Mm... like, Swedish law firms. Yeah. Have you seen a difference between working with, like, Swedish law versus, like- Yep... American law firms- Mm... that sort of thing? And you guys are mostly working with, like, big, big law firms, right? Well, we're also g- smaller law firms also. Oh, okay.

No, we st- we definitely started really, really big law firms, but 50 lawyers also- Okay... will use us and, and get a lot of value. Yeah. So have you seen differences between working with, like, the Swedish ones- Yeah... versus American ones? Maybe, like, even- Mm... I don't know, Asian ones? [laughs] Yeah, yeah.

I think the European ones, um, have- Been a bit slower to move. The American ones are very forward, um, move really quickly. I think, uh, it's also very high on their agenda of the American companies, more so than the, uh, European ones, I think. It's, like, a strategic super important thing we need to just, like, do a lot here.

The American companies generally, both corporates and law firms, also have a much higher appetite to, like, custom-build stuff. That's, like, a thing that they all want to do. Like, "Hey, we wanna build this, like, huge thing with you guys in, like, structure and whatever way." What's- Where Europeans are much more like, "We'll just take the off-the-shelf and figure it out."

What's an example of something they would wanna build themselves? Integration since they're all-- Like, some of these customers build their own CRM and their own- Ah... uh, document management system and their own all sorts of stuff, and they wanna build that, and then they wanna build triggers that, like, you do a review in, in, in Salesforce, it goes to this one person for whatever reason, and they do something, and then it goes into Lagoon, and then it goes back in.

And there is-- so that's, like, the simple end, and then there are some that are extremely ambitious, where it's, like, a whole new product that they wanna build with us, and I-- you know, we can't really do that. [chuckles] Yeah, yeah. Um, but, but that's definitely a thing where, like, American companies really want, um, to build their own more than European ones do.

And the European ones are, like, hey, like, you know, they give you product suggestions or- Yeah, for sure. I mean- Yeah... we have great collaboration with all of them. Yeah. So they'll, they'll, "Hey, this sucks. This is great. We want more of this." Mm-hmm. And we'll just build a, a product that does that. Um- Okay. So they don't see, like, an existential threat here in their business- I mean, there is-...

whereas the American ones do, or? I don't know if that's the reason that they want a custom-build versus not custom-build. Okay. I think they both know that this is, like, uh, extremely important and key f-to their firm's success- Yeah... and their corporate success. So they-- it's, it's both extremely important.

I think it's just a different way to achieve it. Okay. Another question I think maybe y-you wanna bring this up is, like, product metrics. Like, how do you know- Mm... that, like, you know, we're actually doing something- Yeah... good here? Yeah. Um- Yeah, how do you think about that? I'm assuming that's also related to evals.

So we have the regular product metrics that we care a lot about. Are people spending a lot of time in our product? Uh, do they seem to get a lot of value out of it? Do they renew it? Do they come back a lot? We have really good numbers for an enterprise, uh, software of, like, our DAU over MAU and our, like, uh, you know, hours spent per month in the product are extremely high.

And then we do some sort of-- we do online evals also, where it's like, try to figure out generally, like, do we solve the problem here? Just where with, like, a super cheap LLM message, just try to just, like, have an online metric, and then we can use that also when we do roll-outs of new things. We can just, like, compare feature flag-wise, okay, does it-- did it im-pro-prove or, like, did it catastrophically decline?

Because one thing actually that's might be interesting that's difficult for our business is we can't look at any traces, basically. We can't look at any data 'cause it's all super confidential. And that's, like, extremely frustrating to work with 'cause you, you basically don't know what people are doing.

[chuckles] So it's like, here's an agent, and it can do so many amazing things, and I have no idea what people are actually doing with it. I would've assumed you guys, like, signed some sor-sort of agreement with the- Yeah... law firm, and they were like- I wish... "Oh, yeah, sure." Yeah, I wish. No, but the thing is, like, a lot of the data that they have is not even, even theirs, right? It's their clients. Oh. And it's super confidential, so we can't look at it.

And so basically, we have an evals team that's, like, trying to approximate- [chuckles]... what we think everyone is doing and, like, build that into eval sets. Um- Wow... so that's just, like, a super annoying kink of the business. Can you do paraphrase? So for example, ChatGPT has, like, a slash feedback thing- Mm... uh, that directly fully says the full trajectory. Mm.

But obviously that, that is n-not possible- Yeah... for you guys. Mm. Uh, but you can use an LLM to paraphrase, like, from this real thing- Yeah... make a synthetic thing that is, like, close enough, but change all the names, change all the details- Yeah... but, like, still- Yeah. Yeah. We can't re-- There's, I mean, there's always a, a, a, a range, right, of, like, if you, if you paraphrase all the way down to, like, three values and you pick one of them, then obviously that's fine.

If you, if you just remove the names, then that's may-may-maybe not fine. Yeah. And so, like, we are, um, generally pick the, the most safe way. Okay. So, like, we, we restrict ourselves as most possible 'cause we, you know- Yeah, that's fair enough... there should be no risk at, at all. Yeah. Um, but there are things where it's like, okay, we know that the failure modes are probably these ten or twenty, so we can, uh, classify it into one of those, and that's- Yeah... a helpful signal.

Uh, we can classify it into the ta-types of tasks and the practice areas also, and then, like, we have an idea. It's something that we're always trying to, to figure out how to do in a smart way. Also, like, on the privacy front, I guess, um, I'm assuming even two years ago it must have been, like, insanely difficult to get the law firms to be okay with this. Yes. But, like- No...

how do they think about their data being sent over to Anthropic or- Yeah. Yeah, yeah, yeah... you know? I mean, I- Yeah... it must be, like, a huge thing. This-- All of our infrastructure is on Azure- Yeah... um, primarily for this reason, 'cause, like, when we started, law firms were like, "AWS? No." Yeah. [chuckles] Like, "No way. We don't trust AWS." Law firms don't trust AWS? Yeah, yeah. Why? It's different now.

No, but it-- they were like, "Amazon, Google- [chuckles]... we can't trust them with any of our data." So we basically, we had to host everything on Microsoft 'cause they already have 365. Oh, so they're okay with Microsoft but not- Exactly- Oh... 'cause they've already done, like, whatever security review- Oh... et cetera, for all of their documents and all of their Word and whatever.

So, like, Microsoft is an approved vendor in their mind, and so we had to host everything on, on Azure. And that's changed over the past few years. Mm. So, like, now people understand, okay, we need to get on cloud. We need to do all this kind of stuff. Um, there was also a lot more people talking about on-prem back then. That's also disappeared completely.

People understand that, like, you should just do it in the cloud and, and it's safe, and it's probably safer than what you're doing. Yeah. Um, so that's changed a lot. Yeah. But it was really painful. And by the way, it's only been three years, so, like, it's actually like- Yeah. [chuckles] It's a-- yeah, yeah, it's a huge shift. It's, it's, it's great. I mean, good for you guys.

But yeah, I mean, I don't expect the, the, like, you know, the legal firm, uh, legal industry has somehow ignored this for the last, like, ten years. Uh, yeah. Yeah. [chuckles] I think, I think we needed the AI, uh- [chuckles]... kick in the butt for everyone to understand that we need to change- Yeah... our ways a little bit. Okay, okay.

So also then on, like, open source models now are getting really good. Mm. So yeah, how are you thinking about that with the- Yeah. The-- we're just thinking about, uh, to, to your point, um, just bring our clients along on, like, the Pareto frontier. Yeah. And, like, the, on the, the map of all the models and what they're good at and their latency and their cost and their, their governance and, and, and their, um, regional availability.

How do we just optimize across all of that? So- Some open weights models are great for certain tasks, and then we'll use them there. Um, some we'll post-train, and, and some we'll just use the frontier models. Yeah. You called, uh, you called it model arbitrage. Yeah. Uh, you have different, uh, favors. You, you did some research on this where you were firmly anthropic at the start of the year. Uh- Yeah... Grok is improving a lot.

Um, yeah, you wanna give an example of a model that wasn't ranked highly overall, but, like, you wanna shout it out as, uh, surprisingly good? Yeah, I think not overall good. Gemini, one of the early Geminis- Ooh... is, uh, yeah, extremely good for certain types of, um, uh, transcription- Yeah... and speaker attribution, so we use that for transla- um, transcription actually.

Oh, so you have a, a- Yeah, were you guys transcribing- Like a granola. We don't have-- We actually probably will build, like, a meeting thing. Yeah. But this is just, like, talk to speech, speech to text- Ah... text to speech, just like in, in, um, any wor- any other app [chuckles] that has this. And we do actually also transcribe files. If you upload hearings or whatever- Oh...

we'll, we'll then, like, transcribe them, put them into text, and so we can do all of our analysis on it. No 11 labs. [chuckles] No 11 lab. For DPA reasons. Same thing as, like, yeah, data processing agreement, I should say. So, like, our data processing agreement has very few vendors on it- Yeah... 'cause it's really difficult for us- We just need to keep it- Exactly.

We need to keep it- Tight... super tight- Yeah... on where any data goes. And so, um- I think, no, that, that is one of the benefits of, like, a Gemini and OpenAI where they're like, "You, you come to us, we have most of the things that you need." Yeah. And that is actually very, very valuable. Even if it's like, it doesn't have to be, like, state-of-the-art. As long as it's, like, mostly good enough for all tasks- Yeah... then who cares? Yeah. Like [laughs]... Yeah.

No, absolutely. I should actually shout out also Microsoft's transcription model. Ah. They may want- My, my Transcribe? Yeah. Okay. It's great. Okay. It's better than the old Gemini. Yeah, yeah. Uh, from what I've seen. Yeah, they- So-... they did some good results. I don't know, don't even know, like, I think, like, usually all of them are, like, top two, top three, top four- Yeah...

in terms of, like, the, the Arena scores, and that's good enough. Yeah, exactly. [laughs] And it's like you have all the governance, and it's super scalable, and it's like, then that's probably good enough. Yeah, I was gonna say, like, at this point are pretty much the, uh, speech-to-text models, like, good enough to where you're like, "Oh, one word here or there, if it's off, that's-" No. It- No? No, no.

For su- Like, I've done this a lot. And in- Really?... particularly in legal, there's, like, some-- We, [laughs] we had this thing- [laughs]... where I think it's-- I, I forget if it's, like, in Australia where there's, like, a certain name for, like, a, a person in court, and it would consistently transcribe it as condom. Oh. [laughs] And then [laughs] yeah.

So, so there, yeah, there's, there's certain things that you don't want there. I see. Okay. Yeah, but so usually you run an LLM layer on top of it. Yeah. Uh, and then you, you extract some keywords, and then you run the transcription model again with the keywords. Mm. Like, that's- Mm-hmm... that's kind of my, my workflow so far. Yeah.

Uh, but yeah, if you, if, if you've just done this on any large scale, you will pick up tons and tons of transcription errors on any- Anything... domain that is not, like, [inhales] uh, average day-to-day conversation. Yeah. [laughs] Yeah, absolutely. If there's any sort of, uh, jargon in there- Yeah... it's gonna be tough. Yeah. Like, I think, um, Whisper and Eleven Labs still transcribe, uh, Cloud Code as Cloud Clo-Code. Yeah.

And, like, that's- Yeah, I've, I've actually had that problem... very, very common. Yeah. Uh, so, so, like, that's, that's how you know there's, like, a lot more to improve. Yeah. Absolutely. I do want to cover a little bit on the, the agent product side. Uh, so we've covered a few, like, the, the- Mm... top level stuff, right? We've already named dropped Tabular Review. Mm. Uh, we've named dropped Intake.

Yeah. And you've, you've got some other, uh, stuff that we haven't even, uh, touched on yet. But I just wanna cover the, the big two, let's call it. Yeah. Yeah. Sure. Is it-- Is that-- Is, is that fair to say they are the, the big two apart from the- Agent and Tabular Review? Yeah. Uh, and our- And projects... probably- Yeah... yeah, and our Word add-in, um- Okay...

is also something 'cause that's-- a lot of people work, work inside of Word, and so our Word add-in- Yeah... is, is a huge, and, and Outlook add-in also. Yeah. So I just wanna cover, like, the, the big ones that everyone must know. Yeah. Uh, so Tabular Review is basically like fan out of like, uh, a lot across each document.

You can ask the same question across all, all documents. Exactly. Collect it in a table- Yeah... uh, and then work back and forth. And then you can- Yeah. A million is impressive. Uh, what's any other, like, engineering considerations or performance things? Or like, what, what's hard about this? Really, before we do that- Yeah....

can you just, like, walk through, like, what is the user experience for a lawyer, and then, like, actually using Tabular Review? Okay, so you want to go on, like, a top-level flow? Yeah. Yeah, yeah. Okay. Like, so, like, what are they doing- Mm... like, with Tabular Review? Yeah. And then we can go into that. Sure. Yeah. Yeah. Um, so they-- It's typically, I mean, a good example is just a due diligence, right?

Where it's like we need to review all employment contracts, all the supply contracts, all of whatever. And so employment contracts, you put 10,000 employment contracts in there, and then you, um- Like, each cell is one employment contract. Each row is one- Okay... employment contract. And then for cells, you ask questions, basically. So, like, the first question, the first, uh, no, sorry, per column. So the first column might be what's the termination, uh, period.

And then it's gonna, like, fill out for everyone. It's 30 days or whatever it is. And the next question is like, uh, is there an IP assignment clause or whatever? Next one is like, uh, how much, you know, how much do we have to pay them if we fire them or whatever it is. Like, how much are they getting paid? And then it's gonna fill out the entire thing.

You can also just ask the agent to do this. Like, if you just drop 10,000 files into the agent and say, like, "Do a due diligence on this," it will spin up a Tabular Review and run all of them and wait for it to be done and then analyze the results, and it can then highlight, like, red flags. Okay, this, the CEO is getting paid an outrageous amount, or there's, like, this weird bonus structure. Okay, then, like, obviously we need to look at that.

And then the agent from that can draft a memo to send to the client, or, uh, it can just do the review and then have, you know, a lawyer, the user just be like, "Okay, let's, let's look at this. Here are the red flags." It's also collaborative, so like you can comment on it, um, for other people to look at, et cetera. And why do you choose that, like, uh, spreadsheet format? Yeah.

It's just a lot better to-- It's, it's super easy to work with, work in. Uh, it's really easy to get an overview super quickly of what's going on, uh, and to review it. So all of the stuff that we produce also has citations back to source documents. And so you can basically review it super easily. If you wanna just make sure that everything's right, you can also, like, highlight cells.

These are reviewed or whatever. These are the ones that are flagged, and you can comment and collaborate. That's much easier than any other form factor that we've- Try. Yeah. Mm. I will say that the spreadsheet form factor has, has a lot of pull in some areas, but somehow hasn't really spread. Mm. I, uh, I fi- I find it very weird. Like, Clay is- Yeah... another one on the GTM- Yeah...

side that, uh, you know, very known for the spreadsheets. Yeah. You guys. Uh, I don't really know if anyone else does a, a large spreadsheet AI thing. No, no. There, there was like V7 that, like, tried to launch. There, like, there's like Sheets. There's... Uh, even in finance domain, I don't really have like a, a top name that, like- Mm... does spreadsheets. Does this really well. Yeah. [laughs] No, I, I agree.

I think it's like a great... If you, if you have fan-out, and you do the exact same thing, and you're, you're extracting stuff, it's, it's such a great- Yeah... form factor to, like, understand- Yeah... what's going on. Do you find, like, you must export to Excel, or, like, people are happy to live in your spreadsheet? People are happy to live there. Uh- Uh, do you use ag-Grid or your own? We do use ag-Grid. ag-Grid. Yeah.

That seems to be, like, the leading one. Yeah. I mean, they've done this for like, I don't know, 15 years. Yeah, yeah, they know how to... Yeah. Uh, yeah. Work the list. So, so, okay. Yeah, I mean, I'm, I'm happy on that, that spreadsheet angle. Um- Mm... I mean, I think the, the only other interesting thing for me engineering-wise is the fan-outs. Yep. Engineers maybe can...

are, are well-equipped to understand the trade-offs of like, well, okay, before you, you fan this thing out on a million documents- Mm... maybe test it on, like, two. Mm. Uh [laughs] Yeah. Yeah, yeah. Does that work before you go nuts? Do- Yeah... do they do that? Yeah, they will. So our type of review actually will run the first 10 rows- Yeah, just check, right? Always. Yeah. [laughs] Does this make sense?

Did it, did it show us what we thought it was gonna show us? Yeah. That's a, that really good... Oh, but also for people, when people built this, it's just really good for them to iterate quickly on the first 10 rows until, like, they have a, a... They're getting the results that they want, and then they can go HAM.

Yeah. I am happy to move on to the next thing. Mm. Intake, uh, which is your Slack bot. Yeah, yep. Um, everyone has a Slack bot. Uh- Of course... what's different about yours? So ours is just legal specific. Uh, and what, what happens with ours is it's not actually... It might also... We, we'll probably also build one that's just an interface to the agent, but it's actually specifically to basically, uh, kick off an agent that then adds a task to our list.

So we have this lists thing, which is like Linear first. Uh, for, you know, that's the closest, uh, equivalent. And so it will add it to a specific intake list, and then lawyers and agents can go into Lagora and just be like, "Okay, these are the things that, you know, people are sending to us as our law firm or as our, um, legal team in a big corporate."

We can kick off agents for them. We can collaborate on them, and then we can just, like, mark them as done, and then it gets sent back in Slack. So it's more of a... It's not just like kicking off an agent. It's more of a, like, um, I don't know, storefront thing where it's like, "Hey, here's the ticket. I want this thing reviewed. Okay, now it's, now it's... Here it is. It's back.

No one needs to change anything," and the legal team just sits in Lagora and, like, manages the agents and everything there. I think the, the data structure for this kind of thing is really interesting 'cause it's, like, basically the inbox, right- Yeah... for all these kinds of queries that can potentially translate into a thing, into something else- Yeah... into something else. Um, and like, I, I don't know.

Is it hard to integrate all these with all the other features, or do you just not worry about it? [laughs] It's... No, it's not. I think we have some primitives that work really well. So, like, lists is a great primitive 'cause it's essentially... It's, like, extremely flexible. It's almost like- It's like doing Notion database... doing lists. Yeah, a Notion database.

It's like a Notion database, and so you can basically configure it for whatever you want. And we, of course, invest a lot into making this super scalable and work really well, and you can have triggers on them, automations on them, and it's, like, a great glue to glue everything together 'cause then now you can kick off an agent run based on a list if something happens- Mm...

and then that comes back and populates a field or whatever, adds new documents or whatever it is. And so it's like, just like you might coordinate a bunch of coding agents with Linear or some sort of task management tool, we can do it similarly with lists. And so, like, agents can create lists, and lists can create agents, and- Wow... agents can read lists, and so it's like the agent swarm comes alive. [laughs] Yeah. Do people get excited or overwhelmed by that?

You know, like, I, I feel like, uh- Yeah... this is basically end user programming. Yeah. Uh, you're, you know... You can let people set up crazy automations. Some people get super obsessed by it- Mm... and they become, like, influ- like, you know- Yeah... it'd be cool to have, like, Lagora influencers. Yeah, right. Clay has influencers. Yeah, yeah. Um, is that worth it? Um- Too overcomplicated?

We need the, the flexibility, even if we don't actually expose it to the end user. Let's say this was just all internal. Then it's a really fast way for us to utilize people like, people like our legal engineers to basically build a lot of products extremely fast. So, like, you could build custom, like, fund management dashboards.

You can basically build that on list with, like, your own generative UI, and then you basically have a new product that some people are gonna love, and we can have a marketplace of just, like, a bunch of sp- specialized products that we can scale super quickly 'cause all of our legal engineers can just build new ones.

If they're out with a client, "Oh, you want your own? Easy. Like, we'll just fix one for you." So it's like a lovable inside Lagora a little bit, and if we don't wanna expose it, that's also how we build our own custom features. So we, we made another acquisition in the UK called Wexler, and they're doing fact intelligence, which is for litigation mostly, which is like you upload a million documents, uh, for a lawsuit or whatever, and then we extract facts from those with, like, named entities and dates and whatever.

And then when you have all the facts, you can basically hold them up against each other and see which ones are corroborated and which ones are, um, what do you call? Disagreement. They- Yeah... you know. And that's extremely powerful 'cause then you can also transcribe a court hearing, and you'd be like, "Oh, he said this thing," but actually the email over here says that he was not there. Uh, and it's...

Like, if you see it live, it's, like, so fucking cool. Yeah, yeah. And that we can also build on this primitive. And so, like, we can build a lot of really cool things on this one primitive, basically. That's how we think about it 'cause, like, we have to be... As we scale the company, we just have to solve so many things, and, like...

So, so I think a lot about how do we build a core primitive that we can scale to, to many different end products and create very customizable- Mm... experiences. Okay, so you have, uh... What, what core primitives do you have? Let's just restate for, for people. Lists is a core one. Lists. Yeah. Projects. Super configurable one. Yeah, projects. Yeah. It's basically containers of files, file systems.

Yeah. Um, the agent, of course. You can kick that off and run that in very different configurations. Do you have a name for the agent, by the way? No, we don't. No. Just okay, just intake. No. Okay. Yeah. No, you mean- Just intake... like, we called it- Yeah... like, uh, Leia or- Yeah... like, we gave it a name? [laughs] No. Ooh, yeah, Leia. Yeah, Leia would be cool. Yeah. Leia.

Um, people used to talk about Leia as, like, a, a named entity. Yeah. Um, but yeah, we s- we stopped that. [laughs] Okay. Maybe I'll bring it back. It might be confusing. I don't know. So Lists- Probably... Agent, uh, Tabular Review. Tabular Review. Which, like, we're considering is that actually an instantiation of... Like, you can take this as far down as you want, right?

But those are- Probably the big, the big ones, yeah. Yeah, so I actually wanted to talk a little bit about, like, FTEs, 'cause this sounds- Yeah... kinda like that. And I think Max has talked about that a little bit. That like, okay, when you onboard a new, like, big customer- Yeah... you wanna get them value, like, ASAP- Yeah... so that, you know, they stay on- Exactly... stay on Lagora. Yeah. Yeah. How has that evolved?

Like, when did you guys start that forward deployed team? So we hired our first forward deployed engineer, that was our first hire in New York, also YC, uh, founder, nine months ago maybe. I'm not actually sure. Um- Are they considered-... six months ago?... legal engineers or no? No, no. Okay. All right. True engineers. But we do, it, it is something that I think about as, like, how do we exactly enable that at scale?

Um, and I think a, a really core thing is, like, how can we empower our legal engineers, like how can we empower our power users and our legal engineers to customize the product in a powerful way? And then of course we, we'll still have forward deployed engineers, like, help out, you know, do all the custom integrations and the, the, the scoping and figuring out what's possible, and feed all of that back into core product development.

So that's kind of how I think about it, as like almost a first line, second line defense. [laughs] Yeah. Where it's like legal engineers should be able to customize a lot of stuff and build a great experience, and then where they can't or we need to make core product changes or custom integrations or whatever, we have forward deployed engineers who also then cycle in a little bit behind like, sometimes they'll just work on the product and sometimes they'll work- Yeah...

with clients. Can you walk through like an example? Like, okay, so you- Mm... just got, you just got this new client. Like, what's step number one? Like, are you guys like, "Okay, hey, we have a forward deployed engineer for you guys. They're gonna go build-" Yeah... you know, some kind of product for you." Like how does that work? Yeah.

So it's very different client to client, right? Yeah. But, um, if it's a big client and they're super engaged and they, like, want, they have all these dreams of things that they would want to do or they already have, typically they have already tried to build something and it's failed, and they're like, "This is what we, this is a problem that we'd like to solve," um, then we'll s- we'll basically staff them with legal engineers and a forward deployed engineer potentially, depending on what, you know, their thing is, and then we'll just be like, "Hey, these guys will come.

They'll make you successful. They will sit with all your lawyers and teach them how to use it and, like, figure out all the workflows that you have right now and, and build them into Lagora." And some of that will just be a legal engineer and some of that will require a forward deployed engineer. What is that separation, I guess, uh, if you could like talk about that a little bit?

Legal engineers configure the product as a power user would. Okay. They don't write code. Yeah, yeah. And a forward deployed engineer writes code into production- Mm... basically. I guess that's the, that's the- Yeah... the- What kind of background do the forward deployed engineers normally have? Regular engineers. You said YC founder. Yeah. Founders, regular engineers. They're all- Yeah, like, yeah- Yeah...

Dee was your- Yeah, exactly... like, the guy... Dee was our first, yeah. Two YC companies and worked on Search at Meta. Yeah. She's awesome. Yeah. [laughs] Yeah. [laughs] Pretty- Okay, that's a high bar. [laughs] She's great. Yeah, yeah, yeah, yeah. She's, yeah, first, first boots on the ground in the US. Yeah, so- Yeah.

So what are some examples of, I don't know, workflows or things that these people are building for the clients? Are, do you guys have like- Oh-... oh, these are like-... there's a few things... you know, there's a couple use cases that, like- Mm... all the law firms are like, you know, this is the first thing that our FDs are gonna do when they, when they go in. It's very different. Okay.

Um, some core workflows everyone have, and we can just build in Lagora, and that could be anything from like, um, this is how we handle these types of NDAs or whatever. Mm-hmm. And then it's just like, uh, you activate it, and it's more like skill building really for at least, and most of the FDs, that's more like there's something that we can't do yet, like we need some more flexibility in some of the primitives or whatever it might be, or we just have this really complex system that we need to integrate into, and then they'll go out there and they'll build the integration against our APIs and maybe expand our APIs or whatever to make that work.

And how does the, like, legal s- structure work there? Because you said you guys- Mm... yourselves can't even work on deals. Yes. Um- Yeah... so, like, h- how do they work on client projects? You mean the law firms, how do they work on client projects? Sorry, how- Or how are legal engineers-... how do your FDs- Yeah... work on client projects if you're not- So they don't touch the data still. Oh, okay.

So they'll just build the system. Um- Okay... but they will also be onboarded like an employee sometimes. So, like, they might get a laptop and everything, um- Like, to the law firm?... if they need to. Yeah, yeah. Oh, wow. If they need to. Interesting. That doesn't happen very often 'cause, like- [laughs]... usually they just build an integration and it, like, kinda works, but they, yeah, they can if they need to.

And you only onboard FDs into a customer, I'm assuming, if they're a certain size. Yeah, yeah. Okay. It needs to make sense. Yeah. 'Cause w- our FDs can also work on core product, right? So it's like it's c- a constant prioritization- Oh... thing. Is there a rule, like, uh, I guess does, uh, every, like, you know, so Palantir famously had, like, you know, four days you spend on site. Mm.

One day you come back and, uh- Yeah, yeah... contribute back to products. Anything like that? No. We, we don't- Uh, nothing set? [laughs]... uh, [laughs] I mean, to be fully honest with you guys, it's still, it's still, uh, you know, still early days in a lot of this. I mean, where- Yeah... do you see it headed maybe over the next year?

I do think that it's gonna be more, maybe not exactly like Palantir, but, like, we, I think we will have a larger in constant circulation group of FDs that maybe sit with a client for, like, two months or whatever to build out this one thing, and then they go to the next one, et cetera, et cetera. Yeah, but I, I do imagine us rolling in with legal engineers and forward deployed engineers to, like, work on specific projects. Hmm. Oh. Yeah. The API business is actually, uh, interesting, like, like you said. Mm.

Um, because, uh, you know, that's another rule that you can have where you are CTO maintainer of the Lagora platform. Yeah. Right? Yeah. Like, we will offer everything that we have in the Lagora web app- Yep... as an API, and then you guys can use whatever. Is, is that, is that a, a promise that you make, or? That's not something we're promising now. Yeah. Um- We do have APIs that you can build against- Yeah...

but not the entire surface of the product. That's the curious thing, like is that the strategy? Mm. Or... And obviously, you know, some people will love it because- Yeah... uh, I don't have to learn a new tool. I can just use the, the existing tool I have. Yeah. Right? And then I just, like- Yeah... uh, install Lagora, the- Yeah... for example, with the, the Word document stuff. Mm.

Other than that, you know, I, I do think, like, then you have to maintain it. Exactly. That, that's, so that's been my thing. That's why we've tried not to do it. Uh- Okay... we haven't, we haven't had APIs, I mean, we started our APIs, like, six months ago or something. We fought it for a very long time, mostly because, yeah, I just don't wanna, uh, we, we move so quickly in, in the product that I don't wanna be like slowed down by having to maintain old API versions for like a data model that we've changed.

I mean, now we're bigger and things are different, so now it's, it's a cost that we can take on. But, like, when we were 20 engineers, it's just like we, we move so quickly that we can't do this. I've toyed with this, right? Like, it's just generally good for all companies to have a platform team and a product team. Mm.

Because the product team, you have one, but then you can easily spin up a second one as long as the platform is not super tied to the product. Yeah. Yeah, yeah. Uh, you know, like a, a very simple one is web app, mobile app, right? Yep. Or like web app, mobile app, uh- Yep... RCP and- Yep... and then, like, you know, whatever, like- Yeah... uh, other, other integrations.

Uh, then your FDs just are one more client To the overall platform anyway. Yes. Yeah. And I think, like, even the agent, you can think about it this way. There's, like, a harness team or, like, a, a... that runs the agent- Yeah... and you can even, you can build a product around it with, like- Yeah, yeah... skills and automations and whatever else. Yeah. This- Um...

This is, like, very famously the Amazon, uh, web services mandate. Mm. Like, uh, Bezos was like- Everything's an API... "Guys, no more backdoor access. You only offer other teams your APIs- Mm... the same APIs you offer customers." Mm. "If you don't have it, you build it." Yep. "And then the customers also get it." Yeah. I think, I think that's also changing a little bit.

A lot of, um, a lot of APIs now, like one thing that we're toying with is, like, can the agent be the API? I know it sounds weird, but, like, for some of this stuff, what they actually want to do is they want to trigger certain things to happen. For some part of this, it could-- you could just be... I'm not saying that we're doing this, but just, like, a thing that I'm toying with in my head is, could that just be like, okay, you trigger the agent and ask it to do this thing?

Like, obviously it's way less efficient than if you just had, like, an API that you always had, and it's like, you know. But it's something that I'm toying with is, like, how much of our product can just be the agent versus all the other stuff? Well, you know, unbounded execution and costs and latency. Yeah.

That's the- That's-- Is that the only holdbacks? That's... Yeah, and then I think, uh, deterministic, like, um- Yeah... maybe in, you know, a hundred out of-- or in two out of a hundred times, it's gonna do something whack that we didn't want it to do. We're gonna go into, like, harness and, and model, uh, fine-tuning in a bit, but I think there's a, there's a product question about restraint- Mm... about what you don't do. Yeah.

Because you could do all that, and obviously there's a prioritization issue. Mm. Like- Mm... are, are you at, uh, more at a point of let's, uh, say no to most things- Mm... and then only do a short number of stuff, or no, actually we want to do more things and we need, just need to hire, like, one hundred people? [laughing] I was, I was very strongly the first thing for a very long time.

Yeah. Let's just say no to everything. Like, keep the eng team. I, I think, um, I've said this before, but, like, one of our presentations that, like, after I've been at Lago for half a year, uh, I, I put up this slide of, um, you know, the three hundred Spartans and, and, and- Yeah, yeah... for the movie, and I was like, we're just gonna be twenty engineers for the rest of the company's history, and, like, we're gonna beat everyone who's much bigger.

And we're a hundred engineers now, and it feels like we're way too small. I wanted to do that, but I think the-- in the real world we're in, like, fierce competition. People want so much stuff. It's like, uh, I could be a Puritan and just be like, "No, we have to stay twenty people, and we can only build, you know, whatever." Uh, but then we just lose. Uh, and, like, people would go elsewhere.

So we have to build more. And so I think my job is to figure out how do I build a system where we can build a lot, but actually only-- like, where we can-- the, the customer feels like we've built so much stuff, but we've actually only built a few things. That's, that's how I think about my job right now. Yeah. I mean, primitives is right, you know? Yeah, exactly. I think that's gonna be... That's really powerful.

Do testing new products, just seeing if, like, hey, there's, like, any pull here, and then you just kinda like, you're like, "No, let's just focus on our main thing," or? Ooh. We haven't made... I mean, we've made certain strategic bets- Yeah... but we've not tried anything that we don't have a lot of confidence in already. Mostly because we just have, like, a mountain of demand. Yeah.

That's like, "We want all of these things," and so it's does- hasn't really made sense for us to try to be like, "Hey, what if maybe this thing could work?" 'Cause, like, there's so many people screaming for the other things, and so- Yeah... we mostly, uh, build those. And then sometimes we'll disagree with clients, and we'll be like, "Actually, I think this thing over here will make a lot of sense strategically," and then we'll try to build that anyway. Like, strategically for you?

Yeah, exactly. So what are those things? Or the industry. So one of those things, um, was, uh, the agent, actually. So, like, no one was actually asking for, like, I want it to be, uh, I want the assistant that I'm using to have more agency and do things without me asking it. Yeah. Like, no one's, no one ever.

But we're just like, this is def-definitely where the industry is going. This is gonna empower you to do so much cool stuff. Um, collaboration was another one where we're like, we think the industry is going in a place where you can collaborate inside of where you're working, which is Lago, and not in, like, emails and text messages.

So yeah, there, there are, there are a few of those. And then how... Like, once you say, "Okay, yeah, we want you guys to use agents," I'm assuming, like, internally at the law firm, you know, it's gonna take a lot for them to actually- Mm... learn how to do that. Yeah. And so that's- Yeah... where you kinda like say, "Hey, FD is go in there and teach them what they can do." Yeah, legal engineers mostly. Oh, legal engineers. It would be... Yeah, yeah. Okay, that makes sense.

But they're like some people call them forward deployed legal engineers- Yeah, yeah... if you want, but that's essentially what they are. Um, then they go in, yeah, and help, and help them structure their workflows around, uh, the capabilities of the agent. You guys said previously, I think Max said this- Mm-hmm...

but that early on specifically, you guys weren't fine-tuning your own models. Is that still the case? Yeah. So we haven't fine-tuned any models up until now. Yeah. I think now in the past few months, open weight models have become extremely capable, and so we will do it for certain tasks where we think there's a huge delta between what a frontier model can do at the cost and latency versus what we think a, a post-train model can do.

Legal research is a great example. It's like a, you have to make... It's a, it's a fan-out retrieval problem, and you can make a really effective, uh, model to tho-those queries super quickly and super cheaply at low latency. So specifically you're post-training them to be better at- Very specific-... searching... bounded tasks. Yeah, exactly. Okay. Yeah, yeah. We're not trying to, like, train our own fable- Yeah...

to, like, be the main agent. I, I don't think that's like a... We won't win that, you know- Yeah, yeah... versus Anthropic- Yeah... most likely. There's still a lot of, uh, s- let's say search and retrieval research or RAG research- Mm... to do. Yeah. Uh, that is, like, maybe less glamorous. It's not actually even model training sometimes. It's just- Yeah...

like architecture and pipelines. You, you, you did a case study with, uh, TurboParfer, but, like, you know, there's also, like, uh, do you do late interaction of- Yes, absolutely. Yeah. You, you do that. Yeah. Uh, we actually don't do that. Right. But it's, uh... You're completely right that this is...

Uh, so there's one guy on the team, Song, who's, uh, he just really cares about search, and he c- he basically comes to me all the time, and he's like, "Oh, we should do these things," and, like, "There's these new embedding models," and, like, "We can do this late and late interaction." He actually talked to Turbo teams to get them to build multi-vector so that we could do these types of things. But no, there's loads of, uh, low-hanging fruit generally, even before models.

And even in the harness, honestly, like, if you just wanna create a better output for our clients, there's, like, so much stuff that we can do that has immediate impact before training models. Yeah. Like, what makes a good harness for, like, the law industry, I guess? Like, you guys are making, I'm assuming- Mm... like, your own custom harness. Yeah, yeah. Yeah. For sure. Yeah.

The way that we edit files It's important. The way that we do an analysis is important. Uh, the way you do legal research is all baked into it. So it's like you might have a problem that comes up or you see something. Then it knows how to go and do the legal research to figure out what the answer is- Hmm... what the precedent is. That's all very important. And then of course there's all the governance stuff around it as well. Yeah.

It seems like the search is probably, like, would you say the most important tool that you guys are really customizing? 'Cause- It's- If you can't pull up- Yeah... the right information- Yeah, and then you're- Yeah, yeah... just screwed. You're screwed. It's, uh... Yeah, I think it's, it's, it's, um- You guys are searching- Search... like a lot of documents... and analysis. Yeah.

Yeah. It's search, it's analysis, and it's drafting probably. Okay. 'Cause, like drafting word, drafting huge contracts is actually also not super trivial. Like, what about it is not trivial? 'Cause you have to... Let's say you're drafting a huge, uh, contract from scratch, then you have to find precedents of like, well, how did we do this previously? Do we need this type of clause or not?

A specific law firm, how did they do it- Yes... previously? Yeah. So- Oh... I'm, I am the law firm- Yeah... and I'm about to draft a huge, uh, whatever. NDA is very simple, but like there's, there are contracts that are way more, uh, complex. And then you need to find the right precedent first 'cause, like, lawyers just don't, don't like s- sit and like start typing stuff. Yeah.

They, they figure out what clauses have we used in similar deals, and similar can mean a lot of different things, [laughs] as I'm sure you know in search. It could be like same industry, same type of company, same whatever. Um, and so you need to identify the right precedent. You might have like multiple documents that you then try to figure out whi- which part should I take from what document, and that's actually non-trivial. Okay. So it, it's essentially also a search problem.

Which model's best at drafting? [laughs] So I'll say, I was told I was far from this yesterday, uh, definitely Fable and Opus are extremely chatty. I'm sure you guys have known this. Uh, they're very verbose. So that's not good. That's generally not good, no. Okay. So this is true for- I've noticed Astra is pre- pretty good at writing... Astra is, uh- Yeah... yeah, pretty good at writing. And is it promptable?

Much less, more, more concise. Um- Are you running- For, for-... like 100 different prompts to like- Yeah... be sure? We can... Yeah, we can- Okay... we can do, we can do certain things, but I think the chattiness verbosity is actually a little bit difficult for us to do. Wow. So this is... I think, you know, let's see, but probably the next model from Anthropic is gonna be much better at this. Astra is actually very concise. Yeah, yeah.

And I, I s- assume they, they all have a Slack channel with you and you're telling them this, right? Yeah, yeah. Like, this is- Exactly, yeah. We talk about it... this is not news to them. No, no, no. They know this. All right. Then the other thing that I really care about is hallucination. Mm. Do they make stuff up? So, uh, we have a system of, uh, a citation system where we basically check what they're writing has a link.

So the user has a link they can click, and it highlights with a bounding box, like in the PDF or whatever, where, where this information was taken from. And we can sort of make sure that what it says always is citable somewhere, even multiple layers down. So like if the agent writes something based on a tabular review, you know, it can cite the cell in the tabular view or the row in the tabular view.

So you can actually like click multiple layers down and find, you know, sort of validate it all the way up. Yeah. Are you finding that lawyers are like verifying that these citations are- They-... correct or not? No. In- [laughs] Initially, initially- It's kinda like PR... some of them were, but now... Yeah, exactly. It's the same thing... are you actually, are you actually doing it? It's the same thing.

People shit like hallucinated links to Supreme Court. They don't care. That happens, yeah. [laughs] Exactly. That happens. Uh, yeah. I, I think it's like coding. You know, you have engineers that just don't give a shit and just like send agents, and you have people that like sit and read every comma. Same distinction in law.

There's just like so much that like how can one human do that, right? Yeah. But I think they probably just like we know in code review what to look for. Yeah. Like what is the system or whatever. I think they also know, okay, this is the, the thing. These are the re-... Like this is the thing that's important. Let me just like double-check these things. Yeah.

Then like these sound reasonable, so it's probably fine. Okay. Interesting. Hmm. Just like if you, if you receive something from a junior lawyer, it's the same thing you do, right? Just like, eh, I should double-check this and like- Yeah... this probably is fine. Any other elements of let's call it legal reasoning or what's the, the, the legal frontier that- Hmm...

uh, models are not good enough yet that you want from the next generation of models? Nothing I can think of specifically right now. I think long document reasoning is still difficult. Um- Long, like million token, token context you're saying? Yeah, exactly. You have a huge contract- Yeah... um, and like you're making edits across it. It's a difficult thing to do for a model today even. Hmm.

Uh, and like remember that you changed all these things up here and like the definitions change and that you, the ordering changed and whatever else. Hmm. Um, so the simplest would be one shot. You put- feed everything in and then one-shot output. Yes. You don't do that. You break it up into chunks and then- Yeah... you orchestrate edits. Yeah, exactly. Exactly.

And this is like one of those things that are funny, like that are fun to be part of in this industry, right? So our word add-in was like we, we've done, you know, we've done MapReduce, and we've changed, and there was one big one, and there was like one big one and multiple times. It's like we keep changing- Yes...

the, the scaffolding that we build around as models change. Yes. I, I, I would say I, I, um, I recently... I do a lot of, like, news reporting- Mm-hmm... uh, obviously because of what I do, and I've recently shifted from MapReduce to throw everything into context. The entire thing. Yeah, yeah. And it's like- 'Cause now they're-... it's better. [laughs] Who'd have thought?

Yeah, exactly. But it's also, it's different. I mean, we had this problem with search where it's like you're searching for certain things in huge contracts, and you can MapReduce it, but then it might... If you're summarizing any sort of thing, then it might lose some important nuance- Yeah... in between the chunks, essentially if you just summarize the individual parts up. And so we've...

This has been a problem where it's like we can do two types of search. We can also do the other one where it's like, um, you, you need to have reasoning in, in the different steps there in between. Uh, so I'm trying to think of a really good example here. Yeah. There's, there's some documents where they talk about something at length over like, let's say, two pages or whatever, and then each individual chunk is not necessarily relevant to the search term or what they're trying to figure out.

But together it's extremely relevant. And so, yeah, these are some of the, you know, problems where it's- And it must be hard-... and overall... like you said earlier, to actually make the models better at that because you can't even look at the contracts, right? Exactly. These are real contracts. Yeah, yeah.

So do you guys ever ask them for like, "Hey, can you- Yes... make example contracts- Yes... that are similar to your real ones?" We do. Yeah, yeah. Okay. So we have, we have feedback. We can receive feedback from them if they're- Yeah. Things that the company-... willing to engage. Yeah, exactly. Or they're just like, "Hey, this is like super important to us, and it's not working well."

And then we'll be like, "Okay, great. Can you like anonymize the documents- Yeah... and like the problem, whatever? And we'll r- we- if we can recreate it, then we can fix it." Yeah. I mean, so one version of this is you buy a law firm- Yeah... which some people are considering. Yes. But, uh, there's some we... There's some stuff there on whether- [laughs]...

uh, their data is actually their data or if it's the client's data. Hmm. So if the clients are all gone, then maybe it's fine. I actually don't know. Interesting. Yeah. But it's, uh, yes, it's, it's a way to get a lot of data. Yeah, yeah. People like Google is buying Spirit Airlines. Google, exactly. Yeah. Right?

And like- Same thing Yeah, they bought- Yeah, they bought the- [laughs] Yeah, yeah, for like 10 million- Oh, the data... 7 million or whatever. Oh, yeah, yeah. Yeah, yeah. Like, you know, if you have a failed company, you still have your Slack. You can sell the Slack. Yeah, yeah. 'Cause I think there's like- These are- There's like a YC company that, like, buys your failed company.

Yes, there is. Yeah, yeah, yeah. [laughs] Yeah. Yeah. 'Cause like it's just like, well, I just want, like, real people- Yeah, enterprise... doing work. Exactly. And then, yeah, just, uh, like- Exactly... pull stuff out. Exactly. The problem with buying a law firm data is do... Well, you get all the inputs, the, the real inputs, which is great.

Uh, the outputs are a little bit more difficult because you just get what their end result was, and it's difficult, uh, to figure out if, like, the way... You have to basically, like, uh, reverse engineer how they got there. So in our product when we get real traces from real use, this is extremely high value.

But if we just like got this output and we don't know if it's good or bad or how it was created, it's a little bit difficult. We're gonna wrap up soon. Uh, the last thing I'll point people to is also there's a lot of alpha left on embeddings. If you're- Yeah... if you're a rag heavy work- workload, uh, you're, you're just looking at like MTEB and you're taking like top model, uh, you're leaving a lot on the table.

You should look at like, uh, Voyage or like some of the em- Yeah... uh, custom embeddings. You- Yeah, yeah... you've looked into this. We've... Yeah, yeah. Absolutely. Yeah. So basically don't take default embeddings. Even like OpenAI embeddings, Gemini embeddings, doesn't matter. Customize your embeddings. Yeah. Right? Yes. You, you, you support that? Yes. Ev- I think that's right. Yeah. I think that's right.

We- the only thing is, uh, you need to make sure that you're, you're multi, uh, language. So for us, we, we serve so many languages, so that's gonna be more of a problem. I mean, Europe is the king of this. [laughs] Yeah, yeah. Exactly. Uh, Mistral helpful there? Um, no. [laughs] Ooh. Until- That's- Well, until now. They just raised a ton of money, right? So maybe- Yeah. Let's see.

I mean, you guys raised- They also raised a ton of money a year ago... you gotta be- I don't know... Europe champions. Like- I think we need way more model companies out of the, out of Europe. Yes. [laughs] I just wanted to ask you, like what predictions do you have maybe like over the next year for like the law industry? Uh, ex- uh, we, we asked a little bit of that earlier. Yeah.

I'll modify it to your second question, which is, uh, in two years, will lawyers do their work inside Legora or will, will they use, uh, other tools like- Mm... Claude or your Tragedy- Yeah. Yeah... and call Legora? Like, you know, the API approach. Yeah, yeah, yeah, yeah. I think both. It really depends. I think if you're an, an big enterprise, you might have Claude Copilot, and then you'll...

It's, I think it's similar to like how I use Linear through Claude, but I still go to Linear. I think enterprise will do that. I think law firms will just use Legora- Yeah... um, because that's simpler and better. Yeah. Um- It kind of reminds me of like open evidence for like medicine. Yeah. Like, you're, you're basically that for law. Yeah. Yeah. Exactly.

In coding as well, until a point where people, the labs will cut you off for competing with them. Mm. And this has happened twice. Yeah. Do, do you think that'll happen to law? No, I don't think so. Yeah. I think- Because like, you know, they are re- releasing legal services things. Yeah, yeah, yeah. Right? Um- Yeah, they are. I don't... I mean, we obviously talk to them a lot, but it's also- [laughs]...

it's like, "Hey, are you gonna..." [laughs] Your sales reps will say no. Exactly. They don't make the call. Exactly. That's not their commitment. [laughs] Right? And they don't even know of- like, often what's actually going on. I don't thi- I don't think that's the case. They have so much stuff to capture in knowledge work or in, in, in sort of enterprises knowledge work generally that, like, legal has to be part of that, just like data analytics has to- Yeah...

and sales and marketing and finance. So, like, they have to do things that are good there, but, uh, they're never gonna go as deep as we are. And I think if, if Claude takes a small client, small, uh, you know, corporate clients and they do some of their legal via Claude, like, that's, that's great. That's fine.

But if you're doing a, you know, $400 million M&A transaction, you're gonna want Legora. I had kind of an unrelated question. Mm. But I'm curious, are you guys going to law schools and kinda like getting them to like- Yeah... teach Legora- Yeah, yeah... like in classes? Like, how- It-... does that work?

Yeah, it's, it's a- Are you guys thinking about that? It's definitely a program that we are... uh, I think we've started. Oh, cool. I actually don't know exactly where we are, but we, we do want people in law school to- Yeah... learn to use Legora. Um- Ha- like, are you seeing that, like, law schools are changing their curriculums a lot or not really? I don't know. I, I, I, I- Yeah... wouldn't know actually. Interesting. I would actually go at it a different way.

Uh, you should do scholarships for l- for law school for CS majors, for engineers. We will cover your- After you've done CS- Yes... we'll, we'll put you in law school? We'll co- Y- Yeah. CS plus some software experience- Yeah... we'll put you in law school. Yeah. We'll cover it. Or you do like the MA, like, like masters plus JD programs. Yeah. But the- Yeah... they don't care about the masters.

[laughs] Yeah. Well, I mean- Yeah. [laughs]... I mean, like, 'cause there's a lot of people who didn't do CS undergrad, so then they do like a masters just to kinda like get- Yes. Mm... caught up. Yeah. Yeah. Yeah. I, I don't know. I don't see that- I don't... as, as often. I don't. Yeah. Yeah. People do JD MBA. That's a thing. Yeah. But JD CS, uh, I haven't seen it. Yeah. Happens. Yeah. Yeah. Yeah.

Uh, okay, look, uh, overall, uh, you know, super impressive progress. I mean, congrats on all your success. Like, uh, are you still gonna keep up the pace? Like, uh- Absolutely. Yeah, you know, uh, and I think you're hiring in SF, New York- We're hiring in-... Sweden... yeah, engineering, we're hiring in New York, um, Stockholm and London. Yeah.

Uh, and all other roles we're hiring e- everywhere almost. How many people do you have in New York right now? 100. About 100. Okay. Yeah. And you guys are planning on how many in New York? In New York? Yeah. For n- just like generally? Let's just say, yeah, generally engineering. Um, a lot. As many- I don't-... as you can. Yeah. [laughs] It's, it's, it's aggressive. [laughs] Yeah. Cool.

I mean, like that, that's the home of corporate law, right? As far as like- It is. Yeah, it is... uh, mid America if you consider. New York is, uh- So, like- Yeah... I mean, I'm looking forward to you buying a law firm and like- Yes... you know, becoming- Yeah. Yeah. It, uh- [laughs]... it is, it's super exciting to, to...

I think, like, you know, every industry, especially like the top like doctors, lawyers, coders, you know, they, they all have to have like, you know- Yes... two, three agent labs that like just- Yeah... completely dominate the space. I agree. So you guys have like a great market. Yeah. Good luck with that. Yeah, I think so.

And I think it's only gonna get much bigger as like law changes and get- becomes more, um, available to more people. Yeah. Also, I, I think like, I mean, we're already like super over, but like, uh, the problems that you work on are very like the frontier knowledge work. Yes. You know? Yeah. They're... Okay, like u- unless you're literally like doing like science- Navier-Stokes. [laughs]...

science, Navier-Stokes and science, other than that, like most work is like basically c- uh, converges to legal. Yeah. Like- [all laughs] Yeah. Uh, yeah. It's all legal in the end. [laughs] Yeah. Hiring, finance, everything, like I just have to consult my lawyer, so. Yeah. Yeah, I know. I know. Which is why I think it's a really exciting thing to work on, 'cause like modern society runs in some way on legal.

Yeah. Yeah. Cool. All right, that's it. All right. Thank you. Cool. Thank you. Thanks. [upbeat music]

Building Legal Agents When You Can't See the Data - Jacob Lauritzen, CTO of Legora

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