Y Combinator Startup Podcast - How This 25-Year-Old Built A $675M Legal AI Startup (With No Legal Experience)

Episode Date: August 26, 2025

In this episode of Founder Firesides, YC General Partner Gustaf Alströmer is joined by Max Junestrand, co-founder and CEO of Legora, one of the fastest-growing legal AI startups in the world. In just 13 months, Max and his team scaled from 10 to 100 people, raised $80M, and cracked the challenge of selling to one of the most skeptical industries. Max shares insights on building a successful vertical AI company, selling to conservative markets, and sheds light on what the future of legal tech looks like.

AI is continuously developing super, super quickly, and that means we need to do the same. We're finding that as we go deeper and deeper and deeper in the entire legal software stack, we're also seeing that the line between software and service is blurring. I think that's been one of our strengths as a company to say, we don't know exactly where the future is going,

but neither do you. So let's work together to make sure that we're both winners in whatever happens. Today I'm joined by Max Junis Strand. He's the CEO and co-founder of Lagora. The Gora was in Winter 24 and they are the leading AI workspace helping lawyers and legal professionals do their work. Welcome Max. Hey, thanks, Gustav. It's been 13 months since you did the batch. It's been a really busy year for you. It has. It feels like it was a really long time ago. I feel like I've aged five years in the last one.

For those you don't know, tell us about Lagora. Yeah. What are you guys building? At the agora, we're building the AI powered workspace for lawyers. We're essentially transforming the way that they complete their work. Everything from reviewing, drafting, researching. Essentially, within legal, you've had this incredibly fragmented software space where there was a lot of point solutions. And AI was never good enough to actually work with unstructured text, precedent, legal documents. And when GPT 3.5 got out, that just completely changed the game. So we were quick to build a POC.

And then now we've scaled that all the way to an enterprise-grade system serving tens of thousands of lawyers daily. And those point solutions were basically workflow tools. So what were they before? Because it's been a history of a legal technology industry. That existed before. This is not started right now. No, I mean, legal tech has been a category for a long time.

But it was really unsexy for a long time, I think. And you'd essentially have a broad range of point solutions, everything from templating tools where you would sort of codify a contract to special translation tools or redline tools or research tools. And all of them work with text. somehow and generative AI came into the game and just kind of threw up everything off the table. And then when it landed, you very clearly saw how you could solve a lot for a lot of these use cases with the same underlying tech. So chat TP became maybe eight months prior to you guys starting this company.

Describe that moment. Was that an important moment for the company's founding? We were playing around in AI in legal way before chat TPT and we were using these early models from Bert coming from Google. They were decent in English, but they were just horrendously bad in Swedish. And, you know, the first observation that kind of sparked the founding of the company was one of the co-founder's friend, who was a lawyer, spent four months during a summer just summarizing court cases for a big law firm. We basically saw that GPT 3.5 was released to developers, started building. I think the first thing that we built was a stock option reader that would explain how a stock option contract worked. Useful? Right.

You know, as startup founders with no legal background, that seemed reasonable. And then very quickly, the sort of focus changed to how do we build this more wall-to-wall or end-to-end system that every legal professional wants to work with on a databases. And the first product was really quite simple, especially building for Europe. You've got to go through a lot of hassle to kind of conform with all the data processing requirements. So, you know, all data hosted within Europe, nothing for training, no retention, exemption from human review when you look at the way Asher and AWS is structured. And we kind of jumped through all those hoops and just built a system that was compliant for law firms to work with. And then very quickly, as the general sort of AI platforms continue to develop with chat GBT, with Claude, with Gemini, the requirements for what we had to build to be much, much better, you know, continuously increased.

In some industries or some categories like coding or law, for example, it seems like the models are just magical. Like they do things that the people that were in those industries, industries before could not even imagine be possible. Could you describe sort of like the first time you used Lagora to do something that was magical for a customer and how they experienced it? Yes, I think the first time was when we deployed Lagora into the biggest law or largest law firm in the Nordics, Manheimer Swartling. Their managing partner had a famous saying in the newspaper that AI was more artificial

than intelligent, which was back from the early ML models. Yeah, I mean, a lot of firms burnt themselves buying experience. expensive tools that didn't solve anything. And I came into that meeting, you know, I bring up my laptop and I just ask him, you know, put in a query. And he puts in this legal research query and we've tied Ligora to Swedish legislation with the rag system. Yeah.

And it answers perfectly. And you know, you kind of see it on his eyes like it's the aha moment. And now when we're- Is that your aha moment as well? No, I think my personal aha moment was just using chat GPT generally, right? Like it was amazing. It felt complete sci-fi that you could talk with the computer and it talked back. And as an entrepreneur, you kind of quickly, from that, you understand that, all right, we can

apply it in this space, in this way and in that space, in this other way. And I think for legal-specific, the chat experience I think was always cool, but when we took the same models and sort of applied them differently, one of the first use cases we did was due diligence where you have hundreds or, you know, a lot of documents that you want to review. instead of going through them one by one by one, we just made this large grid where essentially every document represented a row and then you could put your queries in the columns. And as you then put in 100 employment agreements and you ask, does all of them include an IP clause where the company protects its intellectual property and it just starts to rattle and it goes,

yes, yes, yes, yes, yes, yes, yes, no, no, yes, and it always links back to the citation. realize like holy shit this is transformational it's taking tasks which used to be you know days or hours and it's turning them into minutes by the time this is is live you will have announced that you have raised a series B how much did you raise where is 80 million dollars led by iconic and general catalyst and you know grateful for wise's continued participation as well as benchmark and red points what is the the software like so so as a lawyer using Ligora, what does my day-to-day look like? So it's really broken up into two pieces.

The first one is the web application, and the second one is our Word Addin. So we integrate directly into Microsoft Word. Right. So if we start with the web application, the first thing that we had was just a simple chat, chat over your own documents and files. This is quickly developed into its own agent that's able to use a lot of the other endpoints in the app

and also external tools to solve more complex, sort of step-by-step workflows. So you could imagine saying, hey, I want to want to write a memo. And the first step of the memo is to go out and do some research. The second step is to take all that research and conform it into the standard language of the firm. And the third step is to write the report and then output is a report. And does it do all that?

It does all of that. Right. And I think we can talk more about it later, but MCP and the way that you can scale the tool usage of these agents is something that I'm super, you know, keen on and that we're leaning very heavily into because a lot of firms have different needs in terms of how they want to adopt the tools to solve for their specific workflows. And it's different if you work in intellectual property or if you work in restructuring or if you work in corporate or if you work in disputes.

The second piece outside of the chat is, well, the grid that I talked about before. We call it tabular review. It's essentially input any number of files and then input any number of queries and we sort of cross run that across each other. And the big innovation there does not really come from, you know, how do you prompt and work with the model, but it's how do you make this run at scale? Right.

You know, how do you run 100,000 queries in parallel at the same time and make sure nothing breaks, all the citations are correct? There's a lot of chunking, sort of rag searching within the individual documents, because sometimes they're very, very long. And with legal docs, there are certain intricacies where you need to always include things like the definitions.

And there might be cross references within each clause to each other. So taking all of that into consideration, that kind of serves the grid. Looking at the word ad-in, I think you could phrase it as cursor for lawyers. Lawyers basically use Word. This is a known fact for a long time. Yeah, I mean, they draft and they review contracts in Word or PDF form. What we really wanted to do is, similar to Cursor, how do we bring generative AI into the existing work environment of illegal professional, and that means integrating in Word.

Now, the difference is you can't fork Word, and you can't take up all the real estate you want. you're basically conform to this sort of right-hand column. And then you've got to get really creative. It's basically like designing a mobile app almost, because that's all the real estate you get. And the first thing that we built there was just, how do we integrate an assistant or a chat that's able to not only read the document,

but also create edits. So you might say, I want you to renegotiate this MSA for the buyer and do that using this internal checklist that I have or this internal sort of playbook or precedent. And now we've scaled that to not only work in a chat-by-chat basis, but also more extensive workflows. So you can say, here's a contract, I want you to take my playbook that consists of 20 different steps and make sure we negotiate from the starting positions and have different fallbacks included. Do you have a specific example of something that was impossible a couple of years ago for a lawyer?

Like literally you couldn't do it, and now you can do it. Yeah, I mean, I think there's a lot of it, right? the early ML models were really bad at legal language. And what they were really bad at was when the language looked different across documents, right? You could train a system to find, let's say, a change of control clause if it looked the same way across all the documents. But it was really, frankly, bad at finding the meaning of a change of control if the class didn't look that way. And so what the LLMs have allowed us to do is to just take tasks where, especially on large contracting, and large document extraction.

So how do we pull the insights from this? Another one is just redlining. So redlining files within Word against president or playbook, completely impossible. Or take deep research across hundreds or thousands of judgments where you need to conform not only the judgments, but also pull in things like legislation and regulation, all into the same place.

Since the cost of intelligence is going down, it also increases the amount of queers we can do. So one pretty cool thing is embedding, making one search against your own documents and files, making another one on the web, and making another one against court cases and judgments and legislation, and then combining all of it to create effectively like a memo that... Maybe they couldn't afford to do in the past. No, of course not.

It just didn't do it. No. And similarly with due diligence, when if you go way back, it used to be a physical data room. That's why it's called a room. You used to go into the room, you had all the documents and all the contracts, and then you'd sit down and read through all of them. And you had to mark them with a pen. So making and doing a due diligence on a company was really expensive.

And now it's becoming almost a commodity where you're expected to do it, but clients are also not really that excited to pay for very simple contract review when they know that AI can do 99% of it. Wow. So in the time that I've been at YC, we have funded some legal software companies,

But the hardest challenge for all of them was selling to law firms and selling to legal. Like, most of them would end up selling to companies because law firms were just like not possible to sell to. That radically changed just like two years ago. Yeah. Can you tell us sort of like, what do you think changed and how do you do it when you go and sell to one of the major law firms in the world? So for everybody listening, this was also one of the questions that I remember you pushing really hard on during the interview. And I think we were quite contrarian to say, you know, no, it's different this time. Trust us.

Yep. I'm glad we were right. I think the way that we approached the problem was always with this idea of we win if you win. So let's align our incentives with saying as a law firm, this technology is revolutionizing. You're going to need to adopt it in some sense, shape or form. And we want to be that long-term partner. And somehow they know that. Well, so what happens is a lot of legal work is low differentiation.

You know, if you're doing a DD from, you know, law firm X or law firm Y, kind of getting the same deal. And so when you have this perfect equilibrium of services and somebody disrupts that by taking a new approach, clients are quick to switch. Yeah. I mean, clients are under price pressure. They want to be effective. Legal fees are very high. And so if this equilibrium breaks, you are almost four. to adopt it.

And you are incentivized. It's kind of the same as the largest adopted computers. Right. If you're billing by the hour, you could say, well, let's have a person walk to the library, you know, find the right book, you know, find the right cases or the right precedent and use that for whatever work we do. Yeah. Or you press control F.

Right. There's always this dilemma of you want to serve your client in the best way possible because that drives you more revenue over time. Yeah. for a lot of the firms that we work with, their brand, reputation, trust, as always putting the client first is what matters the most. And so a lot of the firms also want to be leaders here.

You know, some of them want to be fast second movers, but many want to be first movers, because they're understanding, if you have this perfect equilibrium and you take a simple type of work that gets disrupted, you should get more market share by moving down quicker. But then it's not a raise to the bottom, right? Yeah. It's a question of, okay, if we take-

Every country has a ranking of law firms, basically. Yeah, right. And it's also not a raise to the bottom in terms of pricing. Because if you pull down, let's say, the cost of a due diligence, you free up more time to spend with a board on advising them on, you know, a really complex merger or a really complex acquisition. And so what typically ends up happening is you're under time pressure. You could do more work, but you just have all this stuff that needs to get done. And that's what AI is really good at. But it's also serving lawyers in very creative ways.

I mean, we've had use cases where, you know, we get a call from somebody and they say, I played a role-playing game with Legora, you know, trying to win this argument. And I'm asking it to act as the other party, right? Wow. There was this amazing situation that one of the Spanish partners at a firm called Perthi Thjorka had, where he went into court. he had put all the evidence and all the documents from the opposing party in Ligora and he was actively querying it during the hearing

and during you know at the time when the other attorney was speaking because then he could immediately interrupt if he found something that was that was wrong and he phrased it very nicely he said when when he goes into the battlefield having Ligora is like having another piece of armor and I thought that was that was very poetically could you use Ligora to do negotiation on your behalf Yeah. So the way that we built that is I think the LLMs by themselves are not good enough for that yet And we can talk about that, but it's it's interesting to to build these products knowing that the models will get better Yeah. And where do you stop? Yeah, right on every feature. But so so that feature in the Gore is called playbooks

a playbook is essentially a collection of rules where you either approve or disapprove something. So you might say for the way that you would sign NDAs here at YC you always want the definition definition within a confidentiality agreement to look a certain way. So you provide the rule, you provide some example language, and then you say, all right, if the opposing party will not accept this definition, we have some fallbacks. So fallback one and fallback two. And you just open a document in the Gwara, you open the playbook and you say, press play. And it goes through every rule and runs it against the contract and it marks it up. Yeah.

So if it does not conform with your playbook, it gives you the suggested language so that it will. And the really cool thing about this is it scales outside of just legal departments. So at Lagora, every sales rep is using Lagora to negotiate NDAs before sending it to our legal team. And we just started working with this very large bank in the Nordics. And it's very quickly moved from the legal team to compliance, to risk, and now to sales. Because everybody can leverage the system. And the cool thing about it is it's not only faster and more accurate, but, you know, you agree on a standard because the legal team then creates the playbook and that becomes the standard

that everybody uses so it actually increases quality and consistency over time. YC's next batch is now taking applications got a startup in you apply at Ycom ycombbiter.com slash apply it's never too early and filling out the app will level up your idea okay back to the video none of you guys when you started were lawyers no so you still are building one of the largest or fastest growing legally actually company in the world. How do you do that? I think at this point I've become a hobby lawyer. But how we approached it was being incredibly humble, humble for the fact that we did not know

the industry. We were quick to create relationships with our early partners where feedback was, you know, happening daily. And I think that's been one of our strengths as a company to say, we don't know exactly where the future is going, but neither do you. So let's work together to make sure that we're both winners in whatever happens. And I think now we, of course, have the privilege of having hired a ton of lawyers into the team that work directly with the product teams and directly with the customers, especially in an industry that is now

going through such big change. It was useful to come in with more naivness, if you will, saying why does it work this way? You know, it could work this way instead. Let's say you're a founder watching this right now. You're like, I want to build a software for logistics or for insurance or find a software for. Is your advice basically you don't need any expertise?

How do you how do you learn about the things you need to learn though? I think my advice is you know learn about them right like we we went into this and the first thing I did was I interviewed 100 lawyers. I had this good hack on LinkedIn. I texted them asking if we could have lunch and I would pay their hourly rate and I could definitely not afford it. Yeah. And none of them would impose that. You would just say oh that's amazing like I'll have the lunch with you anyways. One of the attributes that have been very helpful in my career has been that I'm somebody people want to help.

I think that's a very underrated skill. I think there are things you can do to be more like that. You can be a bit fearless in your approach to people and you can also be very, very thankful and grateful and appreciative of the work that other people help you with. If we hadn't done that, we would not be where we're. are today. And then how do you conduct a lunch with a lawyer when you're starting a startup, you know not that much about law? So you'd sit down like this. You'd go to somewhere decently nice, because again, they make a lot of money. And it took me some time to even

understand that the way that departments work are fundamentally different. Like a transactional lawyer works nothing the way a lawyer within the corporate department works. You just ask them a ton of questions. And I think also giving them something back. So, you know, I'd reach out, they see my, you know, tech background and you try to be, you know, give them nuggets of, oh, that's really cool. What do you think about this? Like, you give them ideas. You make them engaged in wanting to give you advice. And, yeah.

And people generally feel good giving founders advice. Of course. Like, it's like something that you should take advantage of. Yeah. And something that I'm, you know, really happy to do now from the position where we're at. There are some large companies in legal technology. Are you going up against all of them? Or how do you think about the existing market of legal tech?

Right. So there's been a lot of sort of large MNA machines and incumbents in this space for a long time. They're not very popular with the end users. I think they have very far-reaching routes. There's some advantages and data modes and so on that come into play. But effectively what AI has done is really changed the game in terms of how quickly you can ship something and it's created a new category so a lot of again this existing point solutions were in maybe suites of these M&A machines and now a lot of it is becoming

irrelevant very quickly and the cost of billing software is also going down very very rapidly so our ability to out ship or you know out deliver these teams of thousands of engineers with just 30 Yeah, is insane. And so we have instead managed to build a company with, I think at the time of recording, it's about 100, where like our velocity is way higher than companies, you know, 100 times our size. I think that's interesting in and of itself in terms of how we built the company with the last year, because when we came out of YC, we were roughly 10 people, and now we're 100. And that means we've onboarded on average, like two people a week.

and hiring correctly, it's really hard. It's a skill you need to learn. And hiring for velocity, hiring for entrepreneurship and ownership of different products and things, but also scale because the company is growing exponentially. So you need your teammates to scale exponentially as well. If people scale linearly, at some point it's a really large delta, and then things aren't working out anymore.

Do these big companies have lock-in, like the big legal tech companies? So these big companies have a couple of advantages, but I think the disadvantages outweigh the advantages almost 10 to 1. There were very large data advantages and being like an incumbent where you lock in a large contract. But I think the buyers have also changed aptitude here. So we're not seeing anybody want to lock in a five-year contract because the world is moving so fast. Of course. So we instead see them, you know, doing one-year contracts. It sounds like a good motivation for companies moving faster.

It is, yes. But even law firms, right? I mean, they don't want to be locked in with a vendor. So they're doing one or two-year contracts. And as we see them now coming up in a lot of places, they're also looking outside of their existing alternatives. So you might have made a bet back in 2023 or 2024 when it was experimentation days. But now you're looking at what are we going to deploy, you know, more. long term. And there what I'm seeing is yes people look at the technology but even

more so they're they're zooming out and they're looking at your rate of change. They want to work with the partner that's going to get them from point A to point B and they can be different things. It might be we want to be AI first and drive our top line or we want to drive profitability and you know streamline our operations. It can be you know very different motivations. How does you tech stack look like what's under the hood? Internally. Yeah. So building our infrastructure I think from the beginning it was pretty pretty clear that we wanted to be on Azure just because it was the same that our customers were on.

And in the beginning, I think Open AI and GPT was really the only model that you could serve via Azure. Now we have much more options available to us. So we use AWS and Claude and Gemini and GPD and Mistral kind of interchangeably. The biggest thing there has been, how do we build everything in such a way where we can hot swap the models whenever we want and also build it in such a way that the models become better, everything improves. And now we've also looked into classification models where, you know, if you do a simple query, we'll serve you a simple model. If you do

a complex query, we'll serve you a complex model. And that's just because that's just, you know, to keep the margins down, but also, you know, sometimes you don't need a bazooka when you just need a, you know, water gun. So who is the buyer? My understanding is that law firms have, but maybe you can explain to me. Like, there's a bunch of partners and there's other people there too. How is a law firm or a legal team on a company generally constructed and who are there and who buys it and who uses offer? It changes a bit depending on size. So if you start with the biggest firms, of course you have the partner group that kind of runs things, but you very often have an innovation department, which sometimes have more or less influence. If it's a very strong innovation

department, they make their own choices. They procure software and they're responsible for the entire innovation agenda. I've frankly got the most energy out of work. working with the innovation folks who are really smart about these things because there's a lot of people that just want to kind of check the AI box and then others who really want to push things forward. And the interesting dilemma there is they're basically driving efficiency across the stack or across the firm, but they're not the users themselves. However, you might often have innovation practitioners that work in the MNA group or the

disputes group or arbitration. And then they will work with those teams. to drive an upskill. So they will have a very like process-minded way of working. And then they might use Ligora to build use cases for the end users. Because when you work in a big law firm, you need to hit your building targets. Yeah. They work a lot.

Like we grind us startup folks, but lawyers grind as well. Lawyers grind as well. And if you know that there's a way to solve something and it's going to take six hours for you to do that, and you know a way how to do it in six hours, you might not take the chance, the chance of exploring a way how you could potentially solve it quicker or you know with a with a higher quality you'll just conform to the way you're used to working yeah so innovation teams have a huge um opportunity and frankly you know mission to drive that across the firm and if you go down a bit so you have sort of mid-sized firms more often than not you might not have an

innovation department and so it's the partners who are making the move or the decision and what i've found is it's hard to get the entire partnership to buy in. Go deeper on this point. I know a lot of founders is asking me, how do I sell to like a financial firm or law firm or something like that? And it seems like this is the tricky part. It's like you have to convince everybody. You have to convince everybody or you start smaller. Okay.

You say, let's work with this partner and their team and make them rock stars. And then everybody else looks at them saying, what's that guy doing? Right. That looks awesome. We also want in, and then you expand. But the key here is to sell, sell, sort of like, not tub down, but sell to the senior people first.

Right, so there's, it's impossible to do a bottom up motion in our industry because you don't procure software individually. You take it through procurement and you take it through IT. And there's a lot of security checks, there's a lot of data privacy checks that you need to go through in order to actually, you know, serve client data in your systems. You were 23 when you co-funded Lagora. By then, you've already done a lot.

You had some stints at other wicy companies, like multiple different ones. Yeah. What was your background before you start this company? When I was 18 and it was time to apply to college, I actually had two options. I was either going to go down the route of becoming a professional Dota, Dota 2 player, or go to college.

I knew this. And my thinking at the time was, OK, what's the best case scenario in each of the outcomes? So best case scenario in Dota would be to win. the international, the biggest tournament in the world, you make $10 million. That would be amazing. But then I was thinking what happens then? It kind of feels like then life stops. And the best case scenario with going to college was basically this, what I'm doing now.

So I decided to go to college. And when you apply to college in Sweden, you go to one school to do one program. So the engineering university is completely separate from the business university, which I think is really weird. We don't mix at all, which is bad. But there was a hack so that you could make an admission to one of the schools and then kind of pull the admission to make another one or pull your application to make another one and then call them and say that you messed it up and you wanted to get, you know, reapplied. So I ended up making it so that I could go to both universities in parallel. It was a really good timing during COVID to do that because that means when you have two lectures at the same time, you can just have two laptops. A record one, yeah.

Yeah, and there were multiple times where I had like exams at the same time with both universities and you would kind of sit with one camera over here and one camera over here pretending that you were just doing one of the exams. And so like one or two years into it, I was working as a programmer. I was building statistical models for e-sports betting and that was really fun. But I think I also wanted to kind of see what the business side looked like. So I had the privilege of working at a company called Norshgen. It's like YSiv, but for impact, and it's based in Stockholm. And I think I got a lot of

exposure to other entrepreneurs. And what struck me then was, one, a few of them were not super ambitious to build companies that we're doing now, but they sort of had this like five-year plan to conquer Nordics. Yeah. So I think immediately, like I had a different take on it. And then they just short stint at McKinsey and worked at Bamlow and just one week at the pick. Depict was one of those companies, is one of those companies. There was an incredible talent magnet. Yeah. Like some incredible people have come out of the PICT.

Like, Anton from Lovable was one of the founders. But there's a bunch of others. You're starting the GORA, even though we spend a week there. But it's kind of cool how you have these magnets that spun off too much of other in cool companies. No, they're amazing. And we're all good friends in Stockholm. It's a small ecosystem. And it's really fun to kind of cheer on each other as well.

And YC ended in April last year. year. Can you walk us through sort of like the company growth and your personal development in this time? Like you were 10, now you're 100. What happened? We grew really fast and we were also feeling the drag. Yeah. You know, like we took the product to market and, you know, we would sell it in a demo. And when law firms start to buy things after one demo, you're doing something right. And so the rationale was like, we should be doing more of this and we want to do it everywhere all at once. And this is also a space where it's kind of obvious that legal and LLM is a good fit. And so there were a lot of other companies in the industry.

I like to say, like there were so many legal AI assistants and now it just feels like many of them have fallen off and they're emerging a couple of winners. With that rationale, we wanted also to get American capital in the company because we wanted to be able to make the move from Stockholm to the US when the time was right. After we raised the money during our first board meeting, we sat down and I remember I remember the look on some of our board members faces when I basically said, we're not going to sell for the next four to five months. And the reason for that was when we got the chance to onboard a client,

it took a lot of work, took a lot of work to get them to a level of understanding of what they could accomplish in the platform. And also the first experience of a legal professional logging in is the one chance you have. If you mess that up, they're not coming back. And we had a couple of situations. of situations where we'd onboard a lot of people and we had done some misses and we didn't want to ruin that. So we worked really hard on reliability, scalability,

got the system to a place where we could comfortably onboard 1,000 lawyers a day. And once we had that, we kind of let it rip. And that's also when we really started to hire. So we remember maybe 25 in the beginning of October and just six months later, we're now 100. So what we did was we said, okay, we're now going to scale across every market in Europe, and we're going to start scaling towards the US.

And our initial conversations in the US sometime because we were a small Swedish startup. So I made multiple trips back and forth to New York. And now we open up hubs both in New York, London, Stockholm, and we'll have people locally in Spain, France, and Germany. So we've really gone at it and just said, hey, we want to do everything everywhere all at once. and let's do it now. And for you personally and SIGIA, like, what was your experience in that? I think the biggest takeaway and learning is going from being an IC into delegating.

And that move, you know, you know how to do something, but you, that's not going to scale. So you need to teach somebody else to do it. And you need to hire people who are way better than you on a lot of different topics. So one of the early sort of hires that we made were actually another. YC founder and we've ended up Jake yeah and we actually we've scaled the team with a lot of entrepreneurs and that's not only like the skills we're looking for but it's also like the way that we built the company because we're effectively running multiple companies within the company it's sort of like a secret playbook that a lot of YC companies are the best ones are all

following is that the first people you want to hire all former founders and it's kind of actually an advice I got from Paul Graham back in the days is that sometimes you think of a founder that I worked on this company for three years, didn't go well. Am I less attractive in the job market? Like, if you're here, if you're in a startup center, you're actually more attractive in the job market. Because people actually want to work with people like you.

Yeah, and we want to hire them. So it's been amazing. And also the agency and the attitude to problem solving, that's kind of what you're looking for. And then sometimes you need to hire for scale, right? Like now we have a significant sales team. And you need somebody who's seeing the 10, you know, 10 million to 500 million, because that's the journey that we're on.

And my learning from Airbnb, which probably, I'm sure applies to you, is the culture in the beginning is the people that you hire. Of course. And when we've now scaled the hubs, we always send a person from Stockholm with them. It's the best people from the Stockholm office that then travels and setups, the new hubs. You seem like the kind of person who embodied the attributes, you can just do things. So can you tell me how that is reflected in your company? You can just do things.

And when we started building this company, we didn't know anything about. law. I think that was pretty apparent in our first interview. And we, you know, made the right moves from them, from them to the second one where we showed that we could do it. You applied for two different batches. Yeah. The first one didn't go as well. And so about this attribute, it's something I look for in others as well. During a lot of the interviews I do, I often ask the question, you know, what have you done outside of your role for the company? Yeah. And here I'm looking for creativity, ability to spot problems and solve them. and to take responsibility for more things than just the stuff that you're doing.

Right. And I think in terms of starting companies and building the future, because frankly, we need to reimagine a lot of the stuff that we're doing. We don't want people who are bogged down by your boss telling you to do something. We have a very sort of flat organization where, let's say our marketing team, we want generalists who are using AI. to do 10x more work than they could have done in the past. And where you might have needed a 30-person marketing team, you now need five.

And you want those five people then to be complete, you know, yes-sayers and to go out, you know, above and beyond. And that characteristic, I think, is increasingly important as well in an age where if you're really ambitious, you can get a lot of leverage out of tools. Absolutely. So if we fast forward like five or ten years, how does the day-to-day job of a lawyer look like? That's interesting. We think about that a lot, right?

I'm kind of viewing it as you're more and more entering a workspace of reviewing work than actually doing it. And you're managing the expectations from your clients and the expectations and the work from your AI agents. You're effectively instructing them, you're watching them go out and do work, and you're making sure that, everything they're doing is not only correct and sort of at your standard, but you're also managing how that work gets delivered to the client. Because I think, you know, you will always want somebody who knows their stuff. Yes. On this. And there's a big reason for why we're working with lawyers and not with the people who might, you know, use the legal services. Because the lawyers needed and necessary to deliver the end product.

But looking five, ten years ahead in these days is also... It's hard. It's hard, right? If I knew where the AMLs would be 10 years from now. Yeah, we're looking weeks ahead now. Right. Yeah. And that's funny just with our product roadmap.

I tried to do them kind of like many quarters ahead. Yeah. Yeah. It's hard. It's really hard. Do you think that the large AI labs are going to try to attempt at doing law? Maybe not law specifically, but I do feel like they're more and more becoming platform companies rather than model providers.

I mean, Google is building Google workspace with Gemini. Anthropic is running very hard on the MCP idea of building kind of a universal entry point into a lot of applications. I think the expectations on companies like us are pretty clear. You know, whatever comes out of a model lab is kind of expected. And then everything else we're adding on top is kind of like icing on the cake. How does the feel that product market fit? I think the feeling is best summarized by almost like this drag feeling or kind of infinite. You've been pulled into the market.

Right. It's like it literally feels like we have infinite demand. And I think that it's coming from a point of the product is working and it's moved from being in this experimental AI bucket into we are reliant on this for core work that we are delivering. Right now, if something breaks, you know, immediately we get a phone call. Say, hey, we can't do this. Like what's going on? Right. And we fix it.

It's basically been this point of you start out. you hope that what you're doing is the right thing, and you try to get early partners excited about what you're doing. And in the beginning, to be really frank, a lot of people got on with us because they wanted to be on the journey, and they took a bet. And I am so thankful and happy that they did that,

because now we've taken them from point A to point B, and we're continuously scaling from here. So we tell vice companies to move to San Francisco, generally. You decided to not take that advice. Can you just tell us about the thinking here? And maybe like if you have some pros and cons, about not being here. Yeah.

The reason why we stayed in Stockholm was we needed a market to grow in. And if you go to the US, it's not only more competitive, but I think it kind of pushes you into becoming a more narrow company. You start building really horizontal. And then you realize, wait a minute, we're really good at this. So you start to scale it in other markets and you quickly notice, ah we're the best in Finland too we're the best in Denmark and we're the best in Norway and then you scale to Spain France and Germany London and then the States and at that point we had always

you know we had already done 15 new market entries the algorithm where the the method was already kind of established of course the US is a bigger undertaking but we had also then grown from this small fish in a small pond to crocodile or a shark in the bigger on now. So you've raised $80 million, like in mid-May. You open an office in New York. You launched with one of the most famous law firms here in the U.S. It seems like you're trying to position yourself as the category leader of AI law in the world. Yeah, I mean, 100% and I think in many aspects, we're already there. It's for me more of a question around ambition and what's next. It's very easy to say, hey, we see this problem, let's go solve it, and then you get satisfied.

But it feels to me like every time we solve a problem, a new one emerges. And we're finding that as we go deeper and deeper and deeper in the entire legal software stack, we're also seeing that the line between software and service is blurring. AI is continuously developing super, super quickly, and that means we need to do the same. And so in my mind, the category leader in the space does not only build a software they serve as the strategic partner to these large firms and they make them win in this transition because it's a very large transition and that's also why we've basically scaled how to headcount as as quickly as we've could whilst

maintaining kind of culture urgency and velocity so a lot of founders that I meet are asking me questions about how you build a vertical AI company that seems like the kind of companies people are building now you have in general advice you want to give to those founders who are just starting out the first kind of obvious tip is don't get locked in with a provider and don't compete with the AI labs. The AI labs ship, right? And so does companies like perplexity and others. And so I think you want to be really clear and honest to yourself where you're adding value

and where you're adding long-term moat. And this is something that we've thought a lot about at Ligua. Like how do we build things as boats so that when the tide rises, just everything gets better. If you're just starting out, you've got to realize that you do not have the capacity to outperform any of those companies. You can have to find a narrow category to do it, where you know the models won't get to. Either that or finding out a way to leverage the models very creatively.

I mean, in a way that others haven't done it, I think take AI scribing. It's a good one. Like, typical AI scribing is hard to do. And you need to embed a lot of custom prompts and ways to get it right so that it uses the right medical language, which is very similar to law. you needed to write clauses in a way that a lawyer would write a clause.

Right. Not just what the model spits out as the most probable answer. If I'm watching this video and I'm like, I'm thinking about applying for a job of the Ligora, tell me about what I should expect, either from the application process or from working there. The things that we look for are ambition and the willingness to say we got this huge problem, there's this huge mountain, how do we climb it? And we're also very upfront with candidates that this is not a 9 to 5.

and we're not the traditional Swedish working environment. We have the good stuff, we have the fika. But we have a lot more hunger and, you know, frankly, a lot of higher expectations. And we want that not only for ourselves, but for each other, because we want to grow as people. And we want to grow as entrepreneurs and as a company, as leaders. And I think they're just looking at like our application process, the biggest thing is, we do is a lot of cases, right? If you want to come in and work in our go-to-market team, you need to come and pitch us our product. And you need to do a really strong pitch. And,

you know, if you take the engineering team, we basically ask you to build a POC of the GORA. And, you know, we want you to work with AI generated code, but we also want you to be able to explain it. Yeah. And to design systems that scale. And I think Stockholm is a small ecosystem. And so it's also quite easy to make references and see who's actually good and who's been in a company and made them a success, you know, not only was there at the right time. Exactly. And another really big piece is we're hiring all over Europe. So we've had people move from Madrid, from Amsterdam, from Germany, from Paris, all the way to Stockholm. We tend to not onboard them in November when it gets nasty, but I feel like we've started to build this

sort of AI hub together with many other companies that is not only like super fun but also you know great companies come out of it thank you so much for coming back to yC thanks kusta thanks kusta