In this episode, we chat with Jacob Lauritzen, CTO of Legora, following the announcement of their $550M Series E at an $8.8B valuation a couple weeks ago. We talk about what it takes to build AI agents for lawyers and what other people building agents can learn from that work.
Legora started in a Stockholm basement with a simple product: ChatGPT, Swedish law, and a search tool. Jacob had already been through YC with his own startup when he helped Max Junestrand with the application for Leya (now Legora). He later joined as head of engineering and is now CTO.
Today the agent drafts and edits Word documents, reviews thousands of contracts at once, and coordinates work between lawyers, their clients, and other agents. The company passed $100 million in ARR in April, less than 18 months after launch, and now has more than 1,800 customers. It raised $550 million in March at a $5.55 billion valuation, then another $550 million in October at an $8.8 billion valuation. Its latest acquisition, Wexler, was its fifth just this year.
We asked Jacob how you evaluate agents when you can't see your customers' data, which tasks still need better models, how legal engineers and forward deployed engineers split the work, and why the billable hour as a business model is being upended.
What changed for the billable hour?
When Jacob first read Leya's YC application, he had one piece of feedback: people had tried building legal tech a million times, and it never worked. If lawyers bill by the hour, getting faster means earning less. Why would they buy a tool that does that?
What changed, he says, is the pressure on firms. In the US, routine work like small reviews is already shifting to flat project fees. Some firms do it for free to hold on to the client. The money moves to M&A, litigation, and time spent with clients.
We asked whether that means firms will stop needing paralegals and junior lawyers. Jacob says that's not what Legora is seeing. A good junior is worth more today than two years ago because they get so much more done, just as in engineering.
Evals without access to customer data
Most teams improve their agents by reading traces. Legora can't read any. Almost everything users upload is confidential, and much of it belongs to the firm's clients, not the firm.
So the evals team has to recreate the kinds of work customers do. Three engineers and three lawyers build RL environments to simulate the work their customers do. One example is a 10,000-file data room where the agent runs due diligence and has to produce a report that catches the right red flags. The team plots the results on a heat map by practice area and task. A new model often gets better at some kinds of legal work and worse at others.
The team draws on a pool of about 100 legal engineers: lawyers who spent three or four years in big law, often with a CS degree. In production, Legora classifies failures into 10 to 20 known failure modes and runs cheap LLM-based checks behind feature flags.
Those restrictions also determine which infrastructure Legora can use. Everything runs on Azure because when Legora started, law firms wouldn't accept AWS or GCP, and Microsoft was already an approved vendor through 365.
Where smarter models still matter
Legora resisted a model picker for a long time before giving in. Jacob's view is that a lawyer shouldn't have to decide whether a contract is a Sonnet-high or an Opus-mid task. It's Legora's job to find the best available tradeoff between quality, cost, latency, governance, and regional availability.
He estimates that models already handle about 60% of legal work really well. For those tasks it's a cost and latency problem now. Complex analysis and editing across million-token contracts still need more intelligence.
Legora has started post-training open-weight models for narrow tasks like legal research, with no plans to train its own frontier model. For drafting, Jacob finds Fable and Opus too verbose. Astra is more concise. To address hallucinations, everything the agent writes gets a citation that highlights a bounding box in the source PDF, multiple layers down.
A few building blocks, many products
Six months into the job, Jacob showed the team a slide of the 300 Spartans and declared that Legora would stay at 20 engineers forever. It now has 100, and he says that still feels too small. His job now is to build a system where the team can ship just a few things and customers feel like they're getting a lot.
The team builds around a few primitives: projects that hold files, the agent, Tabular Review, and lists. Lists are a flexible Notion-style database with triggers and automations. Agents can create and read lists, and changes to a list can start an agent. Intake is a Slack bot that turns a request into a ticket on a list.
Tabular Review is the one the industry copied, down to the name. Each row is a document, each column is a question. Drop in 10,000 employment contracts and ask for the termination period, the IP assignment clause, and the severance terms. It supports up to a million cells, but starts with just 10 rows so you can fix your prompts before running the rest.
Legal engineers configure new products on these primitives without writing code. Even Wexler's fact intelligence product runs on the same building blocks.
Legal engineers first, forward deployed engineers second
Legora hired its first forward deployed engineer about nine months ago in New York: Di Qi, a two-time YC founder who worked on search at Meta. Legal engineers configure the product the way a power user would and don't write code. Forward deployed engineers write code into production, build integrations into a firm's CRM or document management system, and push what they learn back into the core product. They still don't touch client data.
When a big, engaged client signs, Legora assigns legal engineers to work with them and adds an FDE if the project needs one. Within a year, Jacob expects to have a rotating group of FDEs who sit with a client for a couple of months, build what they need, and move on.
Where this goes
In two years, Jacob thinks big enterprises will use Legora through tools like Claude Cowork, while law firms work inside Legora directly. He doesn't expect the labs to cut Legora off the way they've done to coding companies. For a $400 million M&A deal, he thinks people will still want Legora.
Show notes
Legora — the collaborative AI platform for lawyers, where Jacob is CTO.
Jacob Lauritzen — Jacob on LinkedIn.
Legora's Series E announcement — $550 million at an $8.8 billion valuation, with ARR above $200 million.
"Agents need more than a chat" — Jacob's talk at AI Engineer Europe 2026.
"Connect AI to Billions of Legal Documents" — Jacob's AI Engineer talk with Simon Eskildsen of turbopuffer.
How Legora runs 2,000 parallel agent evals on Modal Sandboxes — the infrastructure behind Legora's eval runs.
Legora acquires Qura — the Stockholm legal research startup Legora bought in April.
Legora is acquiring Wexler — the London startup that extracts and verifies facts across large litigation document sets.
Claude for Legal — Anthropic's legal connectors and plugins, the backdrop to whether the labs will come for legal.
AIE Europe Debrief + Agent Labs Thesis — Swyx on the agent lab playbook he brings up at the start.
Timestamps
• 0:00 Cold open
• 0:40 Meet Jacob Lauritzen, CTO of Legora
• 1:05 Starting out as underdogs in a Stockholm basement
• 1:54 How Legora grew from ChatGPT with Swedish law into a legal agent
• 4:12 The switch from assistant to agent
• 5:23 Which models Legora uses and why it added a model picker
• 7:48 Are today’s models good enough for lawyers?
• 8:20 What agents couldn’t do nine months ago
• 9:18 Testing the agent in simulated worlds and a 10,000-file data room
• 10:07 The evals team and Legora’s 100 legal engineers
• 11:03 When evals didn’t seem worth it and one release took citations from 70% to 99%
• 12:18 What legal practice could look like in five to ten years
• 14:18 Will the law firm partnership model survive?
• 16:40 What happens to paralegals and junior lawyers?
• 18:00 The billable hour and why earlier legal tech fell short
• 19:39 Where the money goes now
• 22:44 How Legora used its own product for acquisition diligence
• 23:59 How Swedish and American law firms differ
• 26:12 Product metrics when you can’t read the traces
• 27:41 Paraphrasing traces and narrowing failures down to 10 or 20 types
• 28:42 Why data privacy led Legora to run everything on Azure
• 30:04 Open-weight models and model arbitrage
• 30:55 Transcription models and why Legora doesn’t use ElevenLabs
• 33:33 The agent, Tabular Review, projects, and the Word add-in
• 34:24 How a lawyer uses Tabular Review
• 35:51 Why a spreadsheet works and what you learn from the first 10 rows
• 37:53 How the legal intake bot works in Slack
• 39:11 How agents create lists and lists start agents
• 40:15 Building products on those same pieces and acquiring Wexler
• 42:37 What forward-deployed engineers do at Legora
• 43:52 How legal engineers and forward-deployed engineers onboard a big client
• 46:43 What’s next for the forward-deployed team over the coming year
• 47:24 Why Legora resisted building APIs
• 49:26 Can the agent be the API?
• 50:12 The 300 Spartans slide and the plan to stay at 20 engineers
• 51:32 The bets nobody asked Legora to make
• 53:05 Post-training open-weight models for legal research
• 54:05 Search, retrieval, and late interaction
• 54:51 What makes a good legal agent harness and why drafting depends on search
• 56:25 Which model writes the best draft?
• 57:03 Hallucinations and Legora’s citation system
• 57:41 Do lawyers check the citations?
• 58:19 MapReduce or one big context window for a million-token contract?
• 1:00:16 Getting anonymized examples and the idea of buying a law firm for its data
• 1:01:53 Why Jacob advises against default embeddings
• 1:02:44 Will lawyers work in Legora or through Claude?
• 1:03:26 Will the model labs cut Legora off?
• 1:04:24 Law schools and scholarships for computer science majors
• 1:05:28 Hiring in New York, Stockholm, and London
• 1:06:09 Why most work eventually becomes legal work
Transcript
Automatically transcribed from the revised edit; names and wording may contain errors.
0:00 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. Yep. Junior lawyers, gone. I don't need them. 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, 70% to, like, 99. It's just, like, we did nothing and, like, great. At least you're doing a, you know, $400,000,000 M and A transaction. You're gonna want Legora. We're just gonna be 20 engineers for the rest of the company's history, and, like, we're gonna beat everyone who's much bigger. We're a 100 engineers now, it feels like we're way too small. But it's something that I'm trying with is, like, how much of our product can just be the agent versus all the other stuff. Okay. We're here in the studio with special guests, Jacob Lauritzen, CTO of Legora. Did I get it right? Lauritzen? That's fine. Yeah. That's okay. I'm not Spanish. How... It's Danish. Yeah. Exactly. Does it have a special history? It just... No. Is like son son of. Son of. It's like you have son and... Yeah. So son of Lauritz. That's my name. Nice. We've crossed paths here and there. You you you spoke at AIE a few times, and, obviously, Legora is one of the fastest growing legal tech in history. Yep. And you said something at the start in a pre show that I thought was really interesting. You said that your origin is very European Mhmm. 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 humble about what we're doing because we've always been the underdog from the 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, 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's... It doesn't come naturally to, I think, some of us.
1:30 I kinda wanna start by going over your guys' product, because I feel like with a lot of these, like, legal products, you know, they're kinda like under wraps, like, you know Yeah. You're not supposed to like show it. 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 to where you are today? I think that would be really interesting. Yeah. Absolutely. The way everything started was ChatGPT with Swedish laws 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 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. And so that has always been part of our product. Basically, we started from, like, an 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 more features to it. So one of our features is called tabular view, which is now a name that's being used in industry, and we're very happy to see Claude for Legal using our terminology for what they're building. 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 agreements, which you do in due diligence and when you raise money or whatever.
3:00 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 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. When did you guys start doing or building the agent? 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. Yeah. So this is a rough timeline for people who are understanding YC 2023. Yes. I know you are you are YC founder as '24.
4:30 YC twenty four. Yeah. I was in YC '23. Yeah. Legora was '24. Okay. And then... So so there is... This is, like, a little bit pre a... Pre, like, agents really taking off. Exactly. 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 a video on on the YouTube. It's mostly like ChatGPT for lawyers. Yep. Right? Like, very, like, very fair to say that, but then 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 Exactly. That's exactly what it is. It's it's coworker or ChatGPT, just like really, really good for legal work. And the... Yeah. The the the workflow for, like, either, like, in house corporate lawyers, like, people, like, looking for a coding regulations Yeah. All these things. And we can talk about 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 bunch of different ones depending on tasks. Right? That's And then that's not available to the lawyer to see. You guys Or the agent? 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.
6:00 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 labs according to our judgment. Yeah. So our job is to decide for you because you're not... Your job is not to Exactly. We'll always keep you on the Pareto frontier Yes. And make sure that it's it's great all the time, and you don't have to dive into all the details. Because, like, of the things that I've... Exactly to this point, if you go to a lawyer and you ask them, oh, you're gonna draft this contract based on this precedent, do you think that's a sonnet high or an opus mid task? Actually, they they won't know. Just give me the I've this model. Just, like, make sure it works. Our hourly rate is thousand dollars an hour. Exactly. Just like Make it work. So, yes, I originally came in with that. Was like, oh, yep. This is this is what Devin does differently. And now we have five things in the model picker, including Fable and Opus Yep. Astra. Yes. And people wanna experiment with the fancy new model. Yep. Do you... 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 Coding? Engineering. Yeah. But there are definitely people that would just want table one right away, and, you know, people were asking us about Mythos and everything Yeah. Yeah. Even though I think, realistically, a lot of legal work does not require Mythos level intelligence. Yeah. There is
7:30 So exactly right. Like, I think, basically, 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, 10 x faster than them. Yeah. I would have loved... We we fought introducing it for a very long time, and then in the end, we we just kinda caved in. 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 well right now, and 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. It's like that's the work that we have right now, but also the work with Fort Lauderdale, thus probably expands with intelligence Yeah. I imagine. Early on, like, when you guys first started, I guess, transitioning to the agents Mhmm. Or building the agents, like, what were... Like, what models were you guys using? I guess it would have been like Opus four six or something? That would have been... Yeah. Sonnet four six, Opus four six is probably right. What were the agents, like, not good at at that point? That they're much better at now even though it's only a little, like, nine months later? Sub agent delegation was one of them that we've gotten working really well now for, like, fan out type retrieval queries. Works extremely well for us now. That was difficult before. We basically track... So we have a team that's just doing evals, and so we track all of the model's 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, 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 progress in others. Okay. And that actually happens a lot more than you'd think.
9:00 Like, what are some of those tasks? Like, are you familiar with how you guys are running a lot of the evals? What are those different sub segments, I guess, that you look at? Yeah. So, like, there's different just like framing stuff. So we have different, basically, like, worlds, like RL environments, essentially, which is like, here's a... Because because lawyers work in projects always, more or less, so it's like, here's 10,000 file data room, you're doing your due diligence, in the end, a GRIP report looks like this, like, it contains all these red flags and, like, it doesn't contain whatever weird stuff and, like, we want it to be concise, etcetera, etcetera. That's kind of how we've built it. So there's different tasks, which is, part of it is drafting a report. Like, does the draft look good? Does it contain all the right stuff? Is the wording sound? Is the analysis sound? There's also the part of finding the red flags, like the analysis before that, so we can kinda like subdivide it a little bit, And that's how we run the evals. Interesting. Wait. What does your evals team look like? Like, do you hire lawyers to, like, do that? So, yes, it's a mix. We have engineers, and then we have lawyers, and there'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 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 the industry.
10:30 Are they IT in big law? Or No. They're lawyers in big law. They're lawyers in big law. With CS degrees. Yes. My god. It's a Venn diagram that... Yeah, exactly. That's like our hardest person to hire for, but they're also such a force multiplier because we can pull on them for the eval stuff and for the product development, which is like you have a pool of a 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 eval stuff, and just like help us shape the product, and it's such a such an advantage. And curious, like, did you guys start that team? Evals grew out of the assistant agent team at some point. I think there was a point up until recently where it was like, honestly, it's just not really worth it, because all models just got better. So it was like, do we want to spend a lot of our time doing really complex evals, or do we just want to build... Because there was so much to be gained from building a better harness and sort of leaning into everything's gonna improve, and everything kind of just improved. Mhmm. But, yeah, like, every two, three months, you get a different model. Yeah. Exactly. Mean, we pass it through. Right? Exactly. 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, 70% to, like, 99. It's just like, we did nothing and, like, great. Yeah. Maybe the the the real work is, you know, keeping all the data private, making it taggable, sortable, adding new features like the the tabular view. Is that the, you know You mean over and above ChatGPT? Yes. But obviously, now you're also doing evals. I assume your your use of open models is also increasing. We can talk about that. So, yeah, I I don't know what I'm saying here apart from, like, you know, what is the job? The job is maybe not... In the early days, it was not so much to do do evals because it was mostly exposed model capability to lawyers.
12:00 Yep. The huge part of it is just figuring out what does the legal profession look like in five years, ten years with all of this stuff. Like, coding is maybe a year or two ahead, that 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, v five 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 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 proactive agents that react to stuff when it comes in so you don't have to, you know, kick off everything. So I think a lot of the work is just figuring out what do we think it's gonna look like in five years. If, you know, model 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 it is where we're going. 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?
13:30 I should preface this by saying I don't know. I mean, who knows, really? But I think we can look to coding a lot. I think all lawyers are gonna be way more productive than they were before. They're gonna be able to do a lot more stuff at a higher quality for 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, 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. Just the the partnership model... I don't wanna get 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 manufacturing or whatever. You 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, 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... You know, slavery is abolished. 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 want to 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.
15:00 A lot of firms right now think about, like, well, there's the ex 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 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, that's something that we want to help empower. And does that change the partnership model then? Because 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. Mhmm. Because, like, you get rewarded from, you know, eat what you kill or make this luck step, whatever. That needs to change a little. We need to figure out an incentive structure where partnerships... Partners are incentivized to codify things for... To build an asset for the company, but right now, there aren't because as soon as they leave the partnership, they don't get any dividends from the assets that they built.
16:30 Yeah. So there's like a... Yep. I think the the the whole business model is is... We'll we'll see. Yeah. Yeah. It's all it's all in question right now. Yeah. My my actual other take... You know, so on the partnership level, that's like the top end of the Mhmm. The industry. Actually, my take is more on the bottom end. Right? Yep. What happened there? Paralegal's gone. Yep. Right? Like, junior lawyer's gone. I don't need them. Like, it's That's actually not what we're seeing. So, yeah, it is... I think it's... I know. Would you would you say that we're seeing that in engineering? Yeah. So junior engineers are really kind of cooked. You kinda have to come out of... Drop out of university fully baked or else you're You're not gonna make it. I feel like I'm not... I'm being a bit dramatic for for a comedic purpose. But, like, yeah, I know a lot of lawyers. They started as paralegal. They go to law school, all these things. Right? And now they don't... They're not needed. They're they're seriously just not needed. Yep. Yep. Yeah. It is... It's interesting what happens to the partnership model, like the pyramid. I think generally, a lot of lawyers, you still need your... 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 because they can produce so much more stuff, like, they're good. And I think it's the same thing in legal, where they can actually do a lot of really good work if they're good. So I think it's more that really good lawyers or junior lawyers are worth a lot more. Yeah. It's always been the case. Right? Yeah. Seems like even more But it's, yeah, it's polarizing even more so.
18:00 Yeah. I'm actually curious about that. So, like, the whole, like, law business is basically, like, charging per hour. Mhmm. So Yep. You know, if each project now can be done Yeah. A lot faster is it going to be, like, way cheaper? Like This was actually... So fun story. I was I was reading Max's YC application and, like, helping him getting into YC. Leya, it was called back then, is YC. And this was my one thing. I was like And and curious, Leya is a Danish thing for something? No. Swedish. Swedish? No. Also, it means nothing. There's this Okay. Okay. There's this really funny story in... I think it was a Spanish newspaper that wrote about us in the early days, Leya, and then they they explained that the abbreviation was legally efficient Yoda assistant. Why Yoda? Yeah. I don't know. So I was like, why why is it See, hallucinations are not just from LLMs. No. I think journalists hallucinate. But, no, it doesn't mean anything. So he wrote the application. He he wrote... Yeah. And my feedback to him was, you know, people have tried this a million times. Legal tech has never been a thing because, 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 because the competitive pressure is 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, particularly these, like, sort of lower end stuff on the spectrum, like, whatever it is, like, reviews and whatever. Some of it, honestly, is being done for free, and some of it is just being priced on a flat rate. So where is
19:30 the money being made then? Is it... Because In the strategic work. Yeah. I feel like a lot of what you guys talk about is M and A specifically, like, that's where all the money is a very easy example. Oh, okay. Okay. Litigation is 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, also super important. Like a CRM? No. Just like spending more time with the client. Like, a lot of law firms will, or historically have basically done stuff for free just to have their relationship with the client. Yeah. Yeah. And so now they can do that, but they don't have to lose a lot of money on doing it. I thought I thought I just... I have to pay the lawyer money just to be my friend. Like, in this Yeah. Isn't that the retainer thing? Yeah. Yeah. The... That's You you told me I can do it for free? Like Yeah. You just have to find the right lawyers. Yeah. It's alright. Cool. Or Latent Space has to grow much bigger. I don't know. Yeah. Yeah. We're trying. You know? Yeah. So... Okay. So so so you're saying, you know, obviously, the the strategic work and all these high value add is still there, but our legal fees are not going down. But you also said paralegals are not affected or, like, they're they're just shifting. And and this is something that we also did with... Worry about with Cognition. Like Yep. People worry about the phrasing of, like, we will replace your junior engineer because nobody likes that kind of statement. But in actual fact, yes, like, some some part of it does happen. Other junior engineers embrace it and they become more productive, which is exactly like you just said. So I guess, like, is is there a new term for this role or this this job that is taking... That is having traction with your customers?
21:00 You mean the the Super AI poll junior legal? Nothing I found yet. Maybe we should coin one. Isn't it legal legal That's our... And and also now an industry thing. Yeah. Yeah. CSJDs. Exactly. Yeah. Yeah. Yeah. Yeah. And maybe that's the... There are those other people that are best positioned to do this kind of stuff. I think the the the derogatory term of meat proxy, where you just, like, sit in front of the machine and type in something that 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... Well, you know agents very well, and you you know Legora very well Yep. And you know how to, like like, this is my toolkit, and, you know, every industry has a toolkit Yep. 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. Yeah. It's it's just like I no longer... Like, you know, well, I'll talk about tabular review because that... That's that's a thing that happens a lot. I have hundreds, thousands, millions, thou... And thousands. We do up to a million cells, I think. In a in a single... That's impressive. Yep. We should talk about the the the table and the windowing. Yeah. Least, you know, I I have, like... I'm working on a big case. I have a million documents. I type it in. Usually, I'll throw a pair... Throw, like, a team of paralegals locked in the room. There, so, like, 4AM. Like, that that doesn't need to happen. Right? Because now they can extract it, and you can go and re prompt everything. Mhmm. So, like, yeah, I I think now I don't need a paralegal for that, but, like, a good, like, super AI pilled paralegal would do something else.
22:30 You know? Yes. Yeah. Exactly. Whatever it is. They can do more stuff or, like, spend more time on the things that they, you know, didn't have time for before. Or actually sleep, who knows? No. I have a question. So you guys bought a company, Qura. Yes. Is that how you say it? No. You guys used your own product to do the M and A? Yes. We do that all the time. So how did that work? Like, can you talk little bit about 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. Because right now, we have a Slack workflow where everyone throws their stuff in, and they're like, this is annoying. Yeah. Right? And inefficient, so it's like, great. We should use Legora for that. Yeah. Can you talk a little bit about that? Like, you know, what that process looked like? Yeah. Absolutely. So it's it's... We do a lot of the due diligence ourselves because we can do it with tabular review and other tools. We do still have counsel coming in Mhmm. To, like, assess certain risk or, like, you know, there's and so Qura was simple because it 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. So, like, a lot of the early things we would find, and we have had cases where we found something, 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 these? No, I think so. Alright. Early on, you said you... Your early customers were Swedish? Mhmm. Like, Swedish law firms? Yep. Have you seen a difference between working with, like, Swedish law versus, like, American law firms? Mhmm. That sort of thing? And you guys are mostly working with, like, big, big law firms. Right?
24:00 Well, we're also... Smaller law firms also. Oh, We we definitely started really, really big law firms, but 50 lawyers also 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 Mhmm. I don't know, Asian ones? Yeah. Yeah. I think the European ones have been a bit slower to move. The American ones are very forward, move really quickly. I think it's also very high on their agenda of the American companies, more so than the 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 and, like, structure it in whatever way, whereas Europeans are much more like, we'll just take the off the shelf and What's an example of something they would wanna build themselves? Integrations into their own... Like, some of these customers build their own CRM and their own 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 and it goes to this one person for whatever reason, and they do something, and then it goes into Legora, and then it goes back in. And there is... So that's like the simple end, and there are some that are extremely ambitious, whereas, like, a whole new product that they want to build with us, and I, you know, we can't really do that. Yeah. Yeah. But but that's definitely a thing where, like, American companies really want to build their own more than European ones do.
25:30 And the European ones are like, hey, like, you know, they give you product suggestions or... Yeah. For sure. I mean, we have great collaboration with all of them. Yeah. So they'll they'll say, hey, this sucks, this is great, we want more of this, and we'll just build a a product that does that. Okay. So they don't see, like, an existential threat here in their business? I mean... Whereas the American ones do, or... I don't know if that's the reason that they want a custom build versus not custom I think they both know that this is, like, extremely important and key to their firm's success and their corporate success. So they... It's both extremely important. I think it's just a different way to achieve it. Okay. Another question I think maybe you you wanna bring this up is, like, product metrics. Like, how do you know that, like, you know, we're actually doing something good here? Yeah. 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? 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 software of, like, our DAU over MAU and our, like, 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 with, like, super cheap LMS address, try to just, like, have an online metric, and then we can use that also when we do rollouts of new things. We can just like compare feature flag wise, okay, does it did it improve or like did it catastrophically decline? Because one thing actually that 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 because it's all super confidential. And that's, like, extremely frustrating to work with because you you basically don't know what people are doing. 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.
27:00 I would have assumed you guys, like, signed some sort sort of agreement with the law firm, and they're like, oh, I wish. 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 what we think everyone is doing and, like, build that into eval sets. 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 Mhmm. That directly fully sets its full trajectory. Mhmm. But, obviously, that would... That is not possible for you guys. Mhmm. But you can use an LLM to paraphrase, like, from this real thing Yep. Make a synthetic thing that is, like, close enough, but change all the names, change all the details, but, like, still Yep. Yeah. We can't... There's... I mean, there's always 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 maybe maybe not fine. Yeah. It's like, we are generally pick the the most safe way. Yeah. Because, like, we we restrict ourselves as most possible because we, you know, there should be no risk at at all. Yeah. But there are things where it's like, okay, we know that the failure modes are probably these 10 or 20, so we can classify it into one of those, and that's Yeah. A helpful signal. We can classify it into the tech... 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.
28:30 Also, like, on the privacy front, I guess, 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 Yep. How do they think about their data being sent over to Anthropic or Yeah. Yeah. Yeah. It must be, like, a huge thing. All of our infrastructure is on Azure. Yeah. Primarily for this reason, because, like, when we started, law firms were like, AWS? No. Like Yeah. No way. Don't trust AWS. Yeah. Yeah. It's different now. No. But it... There was, like, Amazon, Google. We can't trust them within our data, so we... Basically, we had to host everything on Microsoft because they already have three sixty five. Oh, so they're okay with Microsoft now? Exactly. Because they've already done, like, whatever security review, etcetera, for all of their documents and all of their Word and whatever. So, like, Microsoft is an approved vendor in their mind, so we had to host everything on Azure, and that's changed over the past few years. So, like, now people understand, okay, we need to get on cloud, we need to do all this kind of stuff. 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 it's and it's safe, and it's probably safer than what you're doing. Yeah. So that's changed a lot. But I was And by the it's only been three years, so, like, it's actually like Yeah. It's yeah. Yeah. It's a huge shift. It's it's it's it's great. Mean, I good for you guys, but, yeah. I mean, I don't expect the the, like, you know, the legal firm... Legal industry has somehow ignored this for the last, like, ten years. Yeah. Yeah. I think I think we needed the AI
30:00 kicking the butt for everyone to understand that we need to change our ways a little bit. Okay. Okay. So, also, then, on, like, open source models now are getting really good. Mhmm. So, yeah, how are you thinking about that with the Yeah. We're just thinking about, to to your point, just bring our clients along on, like, the Pareto frontier. Yeah. And, like, the of the map of all the models and what they're good at and their latency and their cost and their governance and and and their regional availability, how do we just optimize across all of that? So, some open weights models are great for certain tasks, then we'll use them there. Some will post train, and and some will just use the frontier models. Yeah. You call you call it model arbitrage. Yeah. You have different favorites. You you did some research on this, where you were firmly Anthropic at the start of the year. Yeah. Grok is improving a lot. Yeah. You wanna give an example of a model that wasn't ranked highly overall, but, like, you wanna shout it out as surprisingly good? Yeah. I think not overall good. Gemini, one of the early Geminis is, yeah, extremely good for certain types of transcription Yeah. And speaker attribution. So we use that for trans... Transcription actually. You have a Yeah. Why are you guys transcribing? 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, text to speech, just like in in any any other app 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 ElevenLabs. No ElevenLabs for DPA reasons. Same thing as, like, the data processing agreement, I should say. So, like, our data processing agreement has very few vendors on it Yeah.
31:30 Because we just need to keep it first. Exactly. We need to keep it super tight on where any data goes. And so 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, and that is actually 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 Yep. Then who cares? Yep. Like Yeah. No. Absolutely. I should actually shout out also Microsoft's transcription model. They may want MAI-Transcribe? Yeah. Okay. It's great. Okay. It's better than the old Gemini. Yeah. Yeah. Yeah. They they 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 in terms of, like, the the Arena scores, and that's good enough. Yeah. Exactly. And it's like, you have all the governance and it's super scalable, it's like, then that's probably good enough. Yeah. I was gonna say, like, at this point, our... Pretty much the speech to text model is, like, good enough to where you're like, oh, one word here or there, if it's off, that's No? No? No. For sure. Like, I've done this a lot. And particularly in legal, there's like some... We we had this thing where I think it's... I forget if it's like in Australia where there's like a certain name for like a a person in court, and they would consistently prescribe it as condom. Oh. And so there... Yeah. There's there's certain things that you don't want there. Yeah. But... So usually, you run an LLM layer on top of it Yeah. And then you you extract some keywords, and then you run the transcription model again with the keywords. Mhmm. Like, this is kind of my my workflow so far. Yeah. 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 domain that is not, like, average day to day conversation.
33:00 Yeah. Absolutely. If there's any sort of jargon in there Yeah. It's gonna be tough. Yeah. Like, I think Whisper and ElevenLabs still transcribe Claude Code as Cloud Code. Yeah. And, like, it's Yeah. I've I've actually had that. Very common. Yeah. So so, like, that's that's how you know it. There's, like, a lot more to improve. Yeah. Absolutely. I do want to cover a little bit on the the agent product side. So we've covered a few of, like, the of the top level stuff. Right? We've already named job tabular review. Mhmm. We named jobs intake. Yep. And you've you've got some other stuff that we haven't even touched on yet. But I just wanna cover the the big two, let's call it, like Yep. Is it... That is is that fair to say that they are the big two? Agent and tabular review? And our And projects. Probably Yeah. Yeah. And our Word add in Okay. Is also something... Because that... A lot of people work work inside of Word, and so our Word add in 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. Yep. So tabular review is basically like fan out of, like, a lot across each document. You can ask the same question across all all documents Exactly. On the table Yeah. And then work back and forth. And then can Yep. Million is impressive. What's... Any other, like, engineering considerations or performance things? Like, what... What's hard about this? Before we do that, can you just, like, walk through, like, what is a user experience for a lawyer? And then, like Okay. Actually, wanna use tabular view? Yeah. Yeah. Like, so, like, what are they doing, like, with tabular review? Yeah. And then we can go into that.
34:30 Sure. Yeah. Yeah. 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 supply contracts, all of whatever. And so employment contracts, you put 10,000 employment contracts in there, and then you Like, each cell is one employment contract? Each row is one employment contract. And then for cells, you ask questions, basically. So, like, the first question... The first... No, sorry, per column. So the first column might be, what's the termination period? And then it's gonna, like, fill out for everyone. It's thirty days or whatever it is. And the next question is, like, is there an IP assignment clause or whatever. Next one is, like, how much, you know, how much do we have to pay them if we fire them, or whatever it is, like, 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 it can just do the review and then have, you know, the lawyer, the user, just be like, okay, let's let's look at this, here are the red flags. There's also collaborative slide, you can comment on it for other people to look at, etcetera. And why do you choose that, like, spreadsheet format? Yeah. It's just a lot better to... It's it's super easy to work with... Work in. It's really easy to get an overview super quickly of what's going on 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 tried. Yeah.
36:00 I will say that the spreadsheet form factor has has a lot of pull in some areas, but somehow hasn't really spread. Mhmm. I'd... I find I find it very weird. Like, Clay is another one on the GTM side that, you know, very known for the spreadsheets. Yep. You guys... I don't really know if anyone else does large spreadsheet AI thing. No. No. There there was, like, V7 that, like, tried to launch... There's, like, sheets, there's... Even in finance domain, I don't really have, like, a a top name that, like, does spreadsheets. That's just really well... Yeah. 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... You're extracting stuff, it's it's such a great form factor to, like, understand 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. Do you use AG Grid or your own We do use AG Grid. AG Grid. That seems to be like the leading one. I mean, they've done this for, I don't know, fifteen years. Yeah. Yeah. And they know how to... Yeah. So so... Okay. Yeah. Mean, I'm I'm happy on this... That spreadsheet angle. I mean, I think the the only other interesting thing for me engineering wise is the fan outs. Yep. Engineers maybe can... Are well equipped to understand the trade offs of, like, well, okay, before you use fan out on a million documents, maybe test it on, like, two.
37:30 Mhmm. Yep. And... Yeah. Yeah. Does that work before you go nuts? Yep. Do they do that? Yeah. They will. Our tabular view actually will run the first 10 rows. Yeah. Just check. Right? Yeah. Does this make sense? Did it did it show us what we thought it was gonna show us? Yeah. That's a... That's really good. Oh, but also for people, when people build this, it's just really good for them to iterate quickly on the 10 rows until, like, they have 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, intake, which is your Slackbot. Yep. Yep. Everyone has a Slackbot. What's different about yours? So ours is just legal specific, 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 kick off an agent that then adds a task to our list. So we have this list thing, which is like linear, for for... You know, that's the closest equivalent, and so it will add it to a specific intake list, and then lawyers and agents can go into Legora and just be like, okay, these are the things that, you know, people are sending to us as our law firm or as our 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, I don't know, storefront thing where it's like, hey, here's the ticket, I want this thing reviewed. Okay. Now it's not... Here it is. It's back. No one needs to change anything, and the legal team just sits in Legora and, like, manages the agents and everything there. I think the the data structure for this kind of thing is really interesting because it's, like, basically the inbox, right, for all these kinds of queries that can potentially translate into a thing, into something else, into And, something 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?
39:00 It's... No, it's not. I think we have some primitives that work really well. So, like, lists is a great primitive because it's essentially... It's extremely flexible. Also like Notion database. 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, because then you can kick off an agent run based on a list if something happens, 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 coding agents with linear or some sort of task management tool, we can do it similarly with lists. And so agents can create lists, and lists can create agents, and agents can read lists, and so it's like, the agent swarm comes alive. Yeah. Do people get excited or overwhelmed by that? You know, like, I I feel like Yeah. This is basically end user programming. You're... You know, you can let people set up crazy automations. Some people get super obsessed by it, and they become, like, like, you know, it'd be cool to have, like, Yeah. We have influencers. Yeah. Is that worth it? 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 our legal engineers to basically build a lot of products extremely fast. So you can build custom fund management dashboards. You can basically build that on lists with your own generative UI, then you basically have a new product that some people are gonna love, and we can have a marketplace of just a bunch of specialized products that we can scale super quickly, because all of our legal engineers can just build new ones. If they're out with a client, oh, you want your own? Easy. We'll just fix one for you, so it's like a Lovable inside Legora a little bit, and if we don't wanna expose it, that's also how we built our own custom feature. 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 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, what do call it, disagreement.
40:30 They, you know, and that's extremely powerful, because then you can also transcribe a court hearing, and you can like, oh, he said this thing, but actually, the email over here says that he was not there. And it's like, if you see it live, it's like so fucking cool 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. It's 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 experiences. Okay. So you have... What what core primitives do you have? Let's just restate for for people. This a core one. Yeah. Oh, it's core configurable one. Yeah. Projects Yeah. Basically containers of files, file systems. Yeah. The agent, of course, you can kick that off and run that in very different configurations.
42:00 Do you have a name for the agent, by the way? No. We don't. No. Okay. Yeah. No. You mean, like, we called it, like, Leya or it's like we gave it a name? No. Yeah. Yeah. Leya would be cool. Yeah. Yeah. People used to talk about Leya as, like, an a named entity. Yeah. But, yeah, we we stopped that. Okay. Maybe I'll bring it back. It might be confusing. I don't know. So list, Tabular agent 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, FDEs because this sounds kinda like Yeah. That, and I think Max has talked about that a little bit. That, like, okay, when you onboard a new, like, big customer, you wanna get them value, like, ASAP so that, you know, they stay on Exactly. Stay on Legora. Yep. 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 time in New York, also YC founder, nine months ago maybe? I'm not actually sure. They considered legal engineers or no? No. Okay. True engineers. But we do... It it is something that I think about is, like, how do we exactly enable that at scale? 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 will still have forward deployed engineers to, 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. It's like almost a first line, second line defense, whereas, 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 customer situations or whatever, 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 Can with
43:30 you walk through, like, an example? Like, okay, so you 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. Some kind of product for you, like, how does So that it's very different client to client, right? Yeah. But 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, then we'll 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 Legora. 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? If you could, like, talk about that a little bit. Legal engineers configure the product as a power user would. They don't write code. Yeah. Yeah. And a forward deployed engineer writes code into production, basically. I guess that's the the Yeah. What kind of background do the forward deployed engineers normally have at the He said, YC founder. Yeah. Founders, regular engineers,
45:00 old Yeah. Yeah. D was your Yeah. Exactly. D was our first. Yeah. Two YC companies and worked on Search at Meta. Yeah. She's awesome. Okay, that's a high bar. She's great. Yeah. Yeah. First first boots on the ground in the US. Yeah. So what are some examples of, I don't know, workflows or things that these people are building for the clients? Or do you guys have, like, oh, these are like things. You know, there's a couple use cases that, like, all the law firms are like, you know, this is the first thing that our FDEs are gonna do when they when they go in. It's very different. Okay. Some core workflows everyone have, and we can just build in Legora, and that could be anything from, like, this is how we handle these types of NDAs or whatever. And then it's just like, you activate it, and it's more like skill building, really, for Elise. And most of the FDEs, 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 structure work there? Because you said you guys yourselves can't even work on deals. Yes. Yeah. So, like, how do they work on client projects? You mean the law firms, how do they work on client projects? How are Sorry. How how do your FDEs work on client projects So they don't touch the data still. So they'll just build the system. Okay. But they will also be onboarded like an employee sometimes. So, like, they might get a laptop and everything. Like, to the law? Yeah. Yeah. Because they need to. Interesting. That doesn't happen very often because, like, usually, they just build an integration and, like, kinda works. But they, yeah, they can if they need to.
46:30 And you only onboard FDEs into a customer, I'm assuming, if they're a certain size? Yeah. Yeah. Okay. And these two make sense. Because our FDEs can also work on core product. Right? So it's like... It's a constant prioritization thing. Is there a rule, like, I guess, does every... Like, know, so Palantir famously had, like, you know, four days you spend on-site, one day you come back and contribute back to products. Anything like that? No. We we don't. I am... To be fully honest with you guys, it's still it's still, you know, it's still early days in a lot of this. I mean, where 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 FDEs 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, etcetera, etcetera. Yeah. But I I do imagine us rolling in with legal engineers and forward deployed engineers to, like, work on specific projects. Yeah. The API business is actually interesting, like like you said, because, know, that's another rule that you can have, where you will CTO maintainer of the Legora platform. Yep. Right? Like, we will offer everything that we have in the Legora web app Yep. As an API, and then you guys can use whatever. Is is that is that a promise that you make? Or That's not something we're promising now. Yeah. We do have APIs that you can build against, but not the entire surface of the product. That's the curious thing, like, is that the strategy? Or... And obviously, you know, some people will love it because I don't have to learn a new tool, I can just use the existing tool I have. Right? And then I just, like Yeah. Install Legora, for example, with the the Word document stuff. Other than that, you know, I I do think, like, then you have to maintain it.
48:00 Exactly. That's that's... So that's been my thing. That's why we've tried not to do it. 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... 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 when we're 20 engineers, it's just like we we move so quickly that we can't do this. I have to point with this. Right? Like, it's just generally good for all companies to have a platform team and a product team 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 product. Yeah. Yeah. Yeah. You know, like, a very simple one is web app mobile app. Right? Or, like, web app mobile app RTP and and then, like, you know, whatever, like Yeah. Other other integrations. Then your FDEs just are one more client to the overall platform anyway. Yeah. And I think, like, even the agent, you can think about it this way. There's, a harness team or, like, a... That runs the agent, and then you can even... You can build a product around it with, like, skills and automations and whatever else. Yeah. This this is, like, very famously the Amazon web services mandate. Mhmm. Like, Bezos was like A of things in the API. No more backdoor access. You only offer other teams your APIs, the same APIs you offer customers. Mhmm. If you don't have it, you build it. Yep. And then the customers also get it. Yep. I think I think that's also changing a little bit. A lot of a lot of APIs now... Like, one thing that we're toying with is, 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 it's 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?
49:30 Well, you know, unbounded execution and cost and latency. That's the... That's... Is that the only whole bag? That's... Yeah. And then I think deterministic, like, maybe in, you know, a 100 out of or in two out of a 100 times, it's gonna do something whack that we didn't want it to do. We were gonna go into, like, harness and and model fine tuning in a bit. But I think there's a there's a product question about restraint Mhmm. About what you don't do Yep. Because you could do all that, and obviously, there's a prioritization issue. Like, are are you at... More on a point of let's say no to most things and then only do a short number of stuff? Or no. Actually, we wanna do more things, and we just need to hire, like, 100 people? I was I was very strongly the first thing for a very long time. Let's just say no to everything, like, keep the eng team... I I think I've said this before, but, like, one of our presentations that, like, after I've been at Legora for half a year, I I pulled up this slide of, you know, the 300 Spartans and and and Yeah. For the movie, and I was like, we're just gonna be 20 engineers for the rest of the company's history, and, like, we're gonna beat everyone who's much bigger. We're a 100 engineers now, and it feels like we're way too small. I wanted to do that, but I think in the real world, we're in, like, fierce competition, people want so much stuff, it's like, I could be a puritan and just be like, no, we have to stay 20 people and we can only build, you know, whatever, but then we just lose, 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 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.
51:00 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... 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, amount... Enough demand. Yeah. It's like, we want all of these things, and so it doesn't... It hasn't really made sense for us to try to be like, hey, what... Maybe this thing could work, because, like, there's so many people screaming for the other things, and so we mostly 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. What are the ones that we see? One of those things was the agent, actually. So, like, no one was actually asking for, like, I wanted to be... I want the assistant that I'm using to have more agency and do things without me asking it, like, no one no one ever, but we're just like, this is definitely where the industry is going. This is gonna empower you to do so much cool stuff. 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 Legora and not in, like, emails and text messages. So, yeah, there there are there are few of those.
52:30 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 learn how to do that. Yep. And so that's where you kinda, like, say, hey, FDEs go in there and teach them what they Yeah. Legal engineers mostly Oh, legal. Don't be. Yeah. That makes sense. But they're like, some people call them forward deployed legal engineers if you want, but that's essentially what they are. Then they go in, yeah, and help and help them structure their workflows around the capabilities of the agent. You guys said previously, I think Max said this, 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 I think now in the past few months, open weights 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 post trained model can do. Legal research is a great example. It's like you have to make... It's a it's a fan out retrieval problem, and you can make a really effective model through those queries super quickly and super cheaply at low latency. So specifically, you're post training them to be better at? Very specific Yeah. Bounded Exactly. Okay. Yep. 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, versus Anthropic, most likely. There's still a lot of, let's say, and retrieval research or RAG research Mhmm. To do Yeah.
54:00 That is, like, maybe less glamorous. It's not actually even model training sometimes, it's just like architecture and pipelines. You You did a case study with turbopuffer, but, like, you know, there's also... Like, do you do late interaction of Yes. Absolutely. You you do that. Yeah. We actually don't do that, but it's... You're completely right that this is... So there's one guy on the team, Song, who's... He just really cares about search, and he 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 turbopuffer teams to get them to build multivector so that we could do these types of things, But, no, there's loads of 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, like, your own custom harness. Yep. Yeah. Of Yep. Way that we edit files is important. The way that we do an analysis is important. The way you do legal research is all baked into it. So it's like, you might have a problem that comes up or sees something, then it knows how to go and do the legal research to figure out what the answer is, 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, like, customizing? Because It's if you can't pull in the right information Yeah. And then then you're Yeah. Screwed. You're screwed. It's... Yeah. I think it's it's it's You guys are searching, like, a lot of documents. And analysis. Yeah. Yeah. It's search, it's analysis, it's drafting, probably. Okay. Because, like, drafting word... Drafting huge contracts is actually also not super trivial.
55:30 Like, what about it is not trivial? Because you have to... Let's say you're drafting a huge contract from scratch, then you have to find precedents of, how do we do this previously? Do we need this type of clause A or specific law firm? How did they do it previously? Yeah. So I am the law firm, and I'm about to draft a huge... Whatever. NDA is very simple, but, like, there's... There are contracts that are way more complex, and then you need to find the right precedent first, because, like, lawyers just don't don't, like, sit and, like, start typing stuff. They they figure out what clauses have we used in similar deals, and similar can mean a lot of different things. As I'm sure you know in search, it could be like same industry, same type of companies, same whatever. And so you need to identify the right person, and you might have like multiple documents that you then try to figure out which part should I take from what document, and that's actually nontrivial. So it's essentially also a search problem. Which model's best at drafting? Yeah. So I'll say... I was told that was perfect this yesterday. Definitely Fable and Opus are extremely chatty. I'm sure you guys have known this. They're very verbose. So that's not good. That's generally not good. No. I noticed Astra's been pretty good at writing. Astra's... Yeah. Pretty good at writing. Is it much more concise? You've running, like, a 100 different prompts to, like Yeah. Be sure. We we can... Yeah. We can 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.
57:00 Yeah. Yeah. And I I assume they they all have a Slack channel with you and you're telling them this. Right? Like, Yeah. This is not news to them. No. No. They know this. Then the other thing that I really care about is hallucination. Mhmm. Do they make stuff up? So we have a system of... 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 review or the row in the tabular review, so you can actually 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 correct or not? In in in in initially, it's kinda like Some other work, but now... Yeah. Exactly. It's the same thing. Shit, like, hallucinated links to Supreme Court. They don't care. That happens. Yeah. Exactly. That happens. 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 description. There's just, like, so much that, like, how can one human do that? Right? Yeah. But I think they probably... It's 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 red... Like, this is the thing that's important, let me just, like, double check these things, and, like, these sound reasonable, so it's probably fine. Okay. Interesting. Just like if you if you receive something from a junior lawyer, it's the same thing you do, right? Just like, I should double check this, and, like, this probably is fine. Any other elements of, let's call it, legal reasoning, or what's the the the legal frontier that 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. Long, like, million token token contracts is basically You have a huge contract Yeah. And, like, you're making edits across it, it's a difficult thing to do for a model today even. And I remember that you changed all these things up here, and, like, the definitions changed, and that you... The ordering changed, whatever else. So, the simplest would be one shot, you feed everything in, and then one shot output.
58:30 Yes. You don't do that, you break it up into chunks, and then you orchestrate edits. Yeah. Exactly. Exactly. And this is one of those things that are funny, that are fun to be part of in this industry. Right? So our Word add-in, it was like, we've done, you know, we've done MapReduce, we've changed, and there was one big one, and there was one big one in 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 I've recently... I do a lot of news reporting Mhmm. Obviously because of what I do, and I've recently shifted from MapReduce to throwing between these context. It's better. Yeah. 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 a huge contract, and you can map reduce it, but then it might... If you're summarizing any sort of thing, then it might lose some important nuance Yep. In between the chunks, essentially, if you just summarize the individual parts up. So 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, you need to have reasoning in the different steps there in between. So I'm trying to think of a really good example here. 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,
1:00:00 like you said earlier, to actually make the models better at that, because you can't even look at the contracts. Right? Exactly. Do you guys ever ask them for like, hey, can you Yes. Make example contracts that are similar to the real ones? Yeah. So we have we have feedback, we can receive feedback from them, and if they're really engaged, yeah, exactly. Or they're just like, hey, this is, like, super important to us and it's not working well, and then we're like, okay, great. Can you, like, anonymize the documents and, like, the problem, whatever, and we'll... 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 Yep. Which some people are considering. Yes. But there's some... We... There's some stuff there on whether their data is actually their data or if it's the client's data. So if their clients are all gone, then maybe it's fine. I actually don't know. Interesting. Yeah. But it's... Yes. It's it's a way to get a lot of data. Yeah. People like Google is buying Spiritualize. Exactly. Yeah. Bought Yeah. They bought the... Yeah. Yeah. Like, you know, if you have a failed company, you still have your Slack. You can sell your Slack. I think there's like there's like a YC company that buys your failed company. Yes, there is. Yeah. Yeah. Because, like, it's just like, oh, I just want real people doing work. Exactly. And then, yeah, just so it was like Exactly. Pull stuff out. Exactly. The problem with buying law firm data is do... Well, you get all the inputs, the real inputs, which is great. The outputs are a little bit more difficult, because you just get what their end result was, and it's difficult to figure out if, like, the way... You have to basically, like, reverse engineer how they got there. So in our product, when we get real traces from really users, it's extremely high value, But if we just 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.
1:01:30 We're gonna wrap up soon. The last thing I'll point people to is also there's a lot of alpha left on embeddings. You're if you're a RAG heavy work... Workload, you're just looking at MTEB and you're taking, like, top model, you're leaving a lot on the table, you should look at Voyage or some of the custom embeddings. You've gone into this. Yeah. So basically, don't take default embeddings, even OpenAI embeddings, Gemini embeddings, doesn't matter. Customize your embeddings. Yeah. Right? Yes. You you you will support that. Yes. I think that's right. I think that's right. The only thing is you need to make sure that you're multi language. So for us, we we serve so many languages, so that's the world is the king of this. Yeah. Yeah. Exactly. Mistral, helpful there? No. Until... Well, until now, they just raised a ton of money. Right? So maybe Yeah. Let's see. You guys better They also raised a ton of money. You're gonna be your champions. Like I think we need way more model companies out of the... Out of Europe. Yes. I just wanted to ask you, like, what predictions do you have maybe, like, over the next year for, like, the law industry? We we asked a little bit of that earlier. Yeah. I'll modify it to your second question, which is in two years, will lawyers do their work inside Legora, or will they use other tools like Claude or TrejoC and call Legora, like, you know, the API?
1:03:00 Yeah. Yeah. Yeah. Yeah. I think both. It really depends. I think if you're an an big enterprise, you might have Claude Cowork, 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. Think I enterprise will do that. I think law firms will just use Legora Yeah. Because that's simpler and better. Yeah. It kinda reminds me of, like, OpenEvidence for, like, medicine. Like, you're you're basically that for law. Yep. Yeah. Exactly. In coding, it's going to a point where people... The labs will cut you off for competing with them, and this has happened twice. Yep. Do you do you think that will happen to law? No. I don't think so. I think Because, like, you know, they are releasing legal services. Yeah. Yeah. Yeah. They are. I don't... I mean, we obviously talk to them a lot, but it's also... It's like, hey. Are you gonna Your sales reps will say no. Exactly. They don't make the call. Exactly. That's not their goal to make. Right? And they don't even know, often what's actually going on. I don't think I don't think that's the case. They have so much stuff to capture in knowledge work or in in sort of enterprises knowledge work generally, that, like, legal has to be part of that, just like data analytics has to, and sales, and marketing, and finance. So, like, they have to do things that are good there, but they're never gonna go as deep as we are. And I think if if Claude takes small clients, small, 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, $400,000,000 M and A transaction, you're gonna want Legora. I had kind of an unrelated question, but I'm curious, are you guys going to law schools and kinda like getting them to like teach Legora like in classes? Like, how does that work?
1:04:30 Yeah. It's it's definitely a program that we are... I think we've started. I actually don't know exactly where we're at, but we we do want people in law school to Yeah. Learn to use Legora. Like, are you seeing that, like, law schools are changing their curriculums a lot or not really? I don't know. I I I wouldn't know, actually. Interesting. I would actually do it a different way. You should do scholarships for for law school for CS majors, for engineers. We will cover your done CS? Yes. We'll we'll put you in law school? 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, master's plus JD programs? Yeah. They don't care about the master's. Yeah. Well, meaning meaning, like, because there's a lot of people who didn't do CS undergrad, so then they do, like, master's just to kinda, like, get get caught up. Yeah. Yeah. I I don't know. I don't see that as as often. Yeah. Yeah. People do JD MBA. That's a thing. Yeah. But JDCS, I haven't seen it. Yeah. Happens. Yeah. Yeah. Yeah. Okay. Look. Overall, you know, super impressive progress. I mean, congrats on all your success. Like, are you still gonna keep up the pace? Like Absolutely. Yeah. You know? And I think you're hiring in SF New York. We're hiring in... Yeah. Engineering, we're hiring in New York, Stockholm, and London. Yeah. And all other roles, we're hiring everywhere. How many people do you have in New York right now? A 100. About a 100. Okay. And you guys are planning on how many in New York? In New York? Yeah. For just, like, generally? Let's just say, yeah, generally and generally.
1:06:00 A lot. As many as you can. Yeah. It's it's it's aggressive. Yeah. Mean, that... That's the home of corporate law, right, as far as, like It is. Yeah. Mid America's history. So, like, yeah. I mean, I'm looking forward to you buying a law firm and, like, you know, become... Yeah. Yeah. It's it's super exciting to to... I think, like, you know, every industry, especially, like, the top, like, doctors, lawyers, coders, you know, they all have to have, like, you know, two or agent labs that, like, just completely dominate the space. I agree. So you guys have, like, a great market. Yeah. Good. I think so. And I think it's only gonna get much bigger as, like, law changes and get... Becomes more available to more people. Yeah. Also, I think, like... I mean, we're already, like, super over, but, like, the problems that you work on are very... Like, the frontier knowledge work. You know? Yep. They're... Okay. Unless you're literally, like, doing, like Navier Stokes. Science Navier Stokes and science. Other than that, like, most work is, like, basically converges to legal. Yeah. It's all legal in the end. Yeah. Hiring, finance, everything, I just have to consult my lawyer. Yeah. Know. I know. Which is why I think it's a really exciting thing to work on, because modern society runs in some way on legal. Yeah. Yeah. Cool. Alright. That's it. Thank you. Cool. Thank you. Thanks.