Building an AI-Native Software Company With Legora CEO Max Junestrand | Ep. 44

I remember doing this interview in Swedish. There's a saying like blood and

Swedish. There's a saying like blood and smoke and you like you taste the like you taste the blood because you you worked so hard. Yeah. She publishes the article in English at Lorra. We wake up with a metallic taste of blood in our

mouths and people in the company go, "Holy [ __ ] is Max a vampire or does he just floss badly?" Like what's going on?

>> It's not like a culture that I think would quite work in San Francisco. Like

I don't know if that's something that you can do. Well, when we open our San Francisco office, they're going to taste the blood. And yeah, they're going to

the blood. And yeah, they're going to taste the blood.

>> I love it.

>> Well, this is going to be a cool new format. I'm here with my new partner,

format. I'm here with my new partner, Chathan and Max. And Max, you're the founder CEO of Lorra, which is an amazing legal tech company that Chaan sits on the board of. And so, I just

feel really lucky to be doing this with both of you. So, you guys, thank you for thank you for making this happen.

>> Thank you so much, Jack. It's great to be here.

>> Okay, so I want to start with the topic of competition. And Jason, when you

of competition. And Jason, when you invested in the company, there were already competitors out there. This was

I think it was only two. It's crazy

because LGor is like 2 years ago.

>> It's a big company already.

>> Yeah. Almost 400 people, >> you know, but this this the seed was 2 years ago. And at the time of the seed,

years ago. And at the time of the seed, it was an early market, but there were competitors out there. And so I actually want to start with you, Jason, like what was in your head at the moment you invested? Were you thinking about the

invested? Were you thinking about the landscape around like were you just Max is so special that I don't care? Like

what was going through your head when you did that? The first meeting that we had was with Max was with me, Peter and Max actually in the other room. And

interestingly, I had invested in two other legal software companies pre-AI.

Oh wow.

>> And so there was a shape of the legal market that I intuitively understood because I participated in the market.

And so I sort of understood the different kinds of lawyers. Who buys

software? Do in-house lawyers buy software? Do law firms buy soft? how

software? Do law firms buy soft? how

they sort of there was an intuitive understanding that I had and there's sort of like two things that happen when you've like sold into an industry before either you end up hating it or you have some strong bias against it. So there

was always this idea that there's opportunity for AI in the legal market.

And you know there was a player in the market that had already raised at a billion dollar valuation. And when Max came in to chat with me and Peter, the

thing that immediately jumped out was the clarity of thought that Max had on why the general foundation models had a

lot of room to grow in intelligence and how that was going to be a huge boon for the legal profession over the next couple years. And so he had this like

couple years. And so he had this like very strong viewpoint that there was something about legal data that the general models were going to serve in a very unique way.

>> Max, since you're here, can you like explain like what was what what was that?

>> So I think it's it's worth to go back to 2023 and 2024 when I think part of the paradigm was you should train your own models and like the general models

aren't great and fine-tuning is going to be really important for two reasons. We

were like [ __ ] that, right? um one

fine-tuning doesn't really seem to work at least on the scale that we were operating right to train the new generational model you had to put billions of dollars into it and secondly there was so much application that you

had to build on top of the models to make them useful in your environment and back then I mean just solving like basic data compliance privacy and sort of

great file uploads and like great parsing and great chunking and all of these things that was where the value was.

>> And there was another part of your experience which was that you were actually embedded in a law firm. Yes.

>> And so you were studying the shape of like what data law firms had >> in a way that was you know Bill talks about this a lot is like does an entrepreneur strike you as a a learn it

all? And it was clear that the early

all? And it was clear that the early Lagora team when we invested it was like five people. They were just trying to

five people. They were just trying to learn everything they could about how the legal profession worked and they didn't have any bias towards it. And so

the other thing Max said which you should you should share with everyone is because they were embedded in a law firm in a windowless conference room in Stockholm.

>> Sounds nice.

>> Sounds great. Uh they had a deeper understanding I think of like how a law firm like the data model of a law firm in ways that most of us didn't. And I

mean just to take it back even further like when we started um I offered to buy a lot of lawyers lunch on LinkedIn because I wanted to learn. So like

literally coldrite them and I would say hey I'd love to meet I'd love to talk about like IP law. I'll offer to pay you your hourly fee and lunch and they were all too nice to like make me pay for

lunch. uh they'd often end up paying for

lunch. uh they'd often end up paying for it, right? But that did as as Chathan

it, right? But that did as as Chathan put it like I think it allowed us to uh work with customers from the very beginning. So the founding team at Lora

beginning. So the founding team at Lora were all engineers. The first lawyer didn't join until 9 months into the journey.

>> When you had a lawyer join, had you already sort of like set the plan and the goal for the company? Like was that done without experts? And was that important to do without experts?

>> So it's actually funny. I mean the the founding and this is a bit of the Lora untold um like the first reveal the company formation was in 2020

>> and there were four co-founders >> I didn't know that >> and I was not one of them >> didn't know that either >> right they were sort of working on this intersection between AI and law for

three years with the early BERT models and even a Swedish trained version called Suibert but it was impossible to work with uh not only was it not very intellg igent. It was also blatantly

intellg igent. It was also blatantly racist cuz it had been trained on like the Swedish like um like forums. Um >> some racist data there.

>> Some racist data there. When the LLM's like 3.5 came, that was when the moment shifted, right? And so we turned this

shifted, right? And so we turned this into a company. Two of the co-founders left. I joined and then we basically

left. I joined and then we basically said like we're going to work in the intersection between AI and law. We

don't know what that product is, but we're going to run like hell in this direction. And funny enough, the first

direction. And funny enough, the first lawyer who joined was a sort of soon-to-be customer of ours. So he was the CIO at one of the big firms in

Sweden that we wanted to sell into. And

he had built a early version of like a GPT plus the document management system.

So basically uh like an LLM that could rag into the existing president and data that the firm was using. and he

basically said, "Well, these guys are going to run faster than me, and if you can't beat them, I might as well join them." And that turned out to be a good

them." And that turned out to be a good decision.

>> Are you surprised by like how strongly the legal market has adopted AI? If I

had thought, you know, in 2023, let's say, or 24, like what's going to really adopt quickly? Like, I don't know if

adopt quickly? Like, I don't know if personally I would have seen it coming that lawyers would be near the top of the list.

>> I don't know. I mean, you've invested in this stuff before, too, so I guess this is for both of you. But like, has it been a surprise over the last two years, the rate of adoption?

>> Yes, it's been like vivid. But, but

second, and maybe more importantly, um the law firm market is very interesting because it's it's like this perfect equilibrium with frankly like pretty lowiation. Like if you need to do a a VC

lowiation. Like if you need to do a a VC deal here in the valley, like you could go to any, you know, the top five firms and you're going to get roughly the same thing. If one of them starts leveraging

thing. If one of them starts leveraging Lora to offer a better service at a better price point faster, all of them have to adopt it. So the equilibrium

like shifts down and then everybody has to move. So what happened in the law

to move. So what happened in the law firm market was as soon as one big firm in a market uh sort of adopted Lora and went public with it, everybody else had to do the same.

>> Mh.

>> That's not necessarily the same in the in-house legal sector, right? like if

one big bank says it like another big bank doesn't necessarily need it.

>> But was there something about the process of the way work got done or the structure of it that allowed Lorra's product to drive so much value so fast

in a way that it did force that sort of prisoner dilemma. I just think the legal

prisoner dilemma. I just think the legal sector was so underserved with great software for such a long time that there was this like a lot of builtup problems that we could easily solve with LLMs but

they were really hard to solve like prel. I also think you guys had a great

prel. I also think you guys had a great insight early on which was that there was like a a difference in respect to the customers that lawyers are really smart. They're extremely well educated.

smart. They're extremely well educated.

They're techsavvy. They're not

programmers but they're very tech forward. They use like the latest

forward. They use like the latest software. They use the latest devices.

software. They use the latest devices.

And so they were all going to be playing with chatbt and claude. Yeah.

>> And so if you showed up with a legal AI product, it had to be better >> than the foundation models. Otherwise,

they were just gonna say, "Why are you deserving of my dollars?"

>> And Microsoft Copilot rolled out very quickly, like every law firm in the world is a Microsoft shop.

>> Yeah.

>> Like everybody works with Outlook, uh, Microsoft Word, and you know where they store their documents basically. What

have you found it like to the point of you have to be better than the models if you had to break down like as a vertical AI application what have been the things that have

allowed you to just be so much better than the models that it's worth you know the incremental investment >> so I think in the beginning there was a lot of just foundational problems with

the models like you had to guard rail them very hard to make them useful you had to build citations you had to build good rag systems you had to overcome

context window problems and there was a lot of rate limits issues. So you had to like juggle different models for different types of tasks. There's just

so many incremental basic things to solve. I think as time has progressed um

solve. I think as time has progressed um our product has moved further away from what the foundation models are and much more into this enterprisewide platform

where you know we're going to transact billions of dollars of legal work on the platform. And we've moved from building

platform. And we've moved from building a lot of the agent work ourselves and we sort of let the models rip a little bit more like open claw or claw bot or

whatever it's called these days where like with opus 4.5 and opus 4.6 six uh there was an extraordinary difference in in level of intelligence and like

instruction following capability and so I see our job as let's provide the model the right environment and the right tools and skills to leverage and then

let's build a UI and a and an interface to the rest of the business so that they can all leverage it uh comfortably and with a lot of trust. I do think that, you know, the model capabilities

improving so quickly makes us run faster because we have to be three standard deviations ahead of any general capability. Yeah. And that's like a very

capability. Yeah. And that's like a very good motivator.

>> As an as somebody that's invested in a lot of software companies, like one of the unique things about an AI software company is that it's tactically built differently than a traditional software

company. And I think it's becoming more

company. And I think it's becoming more known now, but when you guys first started and you guys built up this org, the way you designed the org made a lot of sense for the product you were

building and what you just described, which is like we need to deeply understand model capabilities and then we need to bring that to our customers in a way that's deeply differentiated, which as you explained to me we need to

invest heavily in understanding the models, which then would lead to understanding what to build. But as

models got better, your features may not matter in six months.

>> Yes.

>> And so talk about how that led to an organization that was heavily technical, heavily engineering and researcher le

and for as a company as big as you you are, you have very few product people.

The number of product people you have essentially rounds to zero. You have

like leaders, you have a couple of leaders, but that's it. I mean the founding team were were three engineers and so you know the the most natural hires were let's grab all the smart engineers that we know from college and

let's add them into the or in the beginning you know we had to build our own agent framework because like langchain and these things that we initially built on like couldn't get customized to the level that we needed

back in 2024 as we understood more about the model capabilities but also of the problems we wanted to solve like let's take due diligence as an example um it's really hard to solve of a due diligence task in a chatbased format because you

need to review hundreds of documents and hundreds of documents are never going to fit into the context window of a single model call at least not back then and you know probably not now either. So we

built this new product that we called tabular review big matrix where you would throw in you know tens of thousands of documents and you'd throw in all the prompts and it started running all of them in parallel and what we basically did was we just said okay

three engineers you're now on tabular review this is your own company run over 10% of the EPD or at Lora are XYZ founders so our head of engineering Jake

who joined he was a solo founder in YC our VP product Adrian was also a legal tech founder in YC and happened to be both GC and a lawyer. And so as we've

progressed, engineering and product has sort of stayed at the core of of who we are and what we do. And I also think that everything else is sort of an expression of that. Like we can only

market what we actually build. We can

only sell what we actually build. And

product lead compounds.

>> I think as as you put it in the beginning, we did not show up first.

Like Lora was not the first product that many legal teams looked at because they were earlier entrance. So we knew that we had to show up and be best and if you want to be best well then you need to

invest in product. You need to invest in engineering. And I think you need to

engineering. And I think you need to build that culture of like reliability first. We actually had a time period in

first. We actually had a time period in the company for for 6 months where we didn't sell basically because we weren't ready to like hit the gas on onboarding

a thousand lawyers a day and knowing that the product was going to keep up with that. So we took the early hits of

with that. So we took the early hits of like investing in that.

>> Talk more about that period specifically. You know the seed round

specifically. You know the seed round you did with us was in March of 2024.

The product went to GA October 1st, 2024.

>> Yeah.

>> And you called me early September 2024 and said, "You need to come to Sweden because all of us need to sit in a room and just talk about where we are and what we need to do to get this thing out

in a month." And you know, we came and we sat the whole literally the whole company, which wasn't that big back then. It was only 10 people. Yeah. And

then. It was only 10 people. Yeah. And

so it was like the whole company, the founders, chicken wings and beer.

>> Yeah. And we and peanuts actually. Those

were the three things served.

>> And there was like a very open dialogue of like how do we get this thing out in 30 days because at that point cuz you were essentially, you know, weren't facing the market test. You were

building there were 10,000 things you could build and that the sort of like outcome of that discussion was that we're only going to focus on three use cases.

>> Yeah, that's right. So talk about like well one you know you calling me to tell me to come to Sweden to have that discussion >> and you actually showing up.

>> I did show reflecting on it that was one of the most important things that that you did in the company the founders did in the company was at that moment say we

have 30 days to go we're just going to sprint at these three things not the 15 things that we could do.

>> Yeah. So I think there was this feeling of like you got these LLMs, they're so powerful. We learn about all these use

powerful. We learn about all these use cases in the firms and with the clients that we work with. Let's go solve all of them. Like wrong decision. Like you

them. Like wrong decision. Like you

can't solve 15 things at the same time.

And so we had to to kill a few darlings and we had to like really double down on the stuff that we thought was going to work. and we looked at on the market and

work. and we looked at on the market and we basically saw a few things that were really working like as a paradigm for LLMs in legal. One of them was this big sort of tabular extraction. Another one

was embedding it deeply into word and outlook. So basically having Lora be

outlook. So basically having Lora be accessible wherever the lawyer is already working. And we were still

already working. And we were still called Leia back then like this was very early. We took the entire company. We

early. We took the entire company. We

had like a town hall and I remember showing some numbers where like a particular company that just had one of these features were doing more revenue than us. We were doing like 1.5 million

than us. We were doing like 1.5 million at the time. That felt very painful because we thought that we had a better suite but we didn't have as much revenue because we were based in Sweden and we were sort of mostly selling to still

European firms at the time. So we just said let's do these three things. let's

do them better than anyone else and it's going to be worth to buy our suite over anybody else's. And so I wrote this like

anybody else's. And so I wrote this like very short product manifesto, send it out to the entire company and we sort of rallied the troops. I think it was off the back of that that we had our first quarter where we doubled revenue. So we

went from like 1.5 to four. We're like,

"Oh, this is like ripping and it's flying off the shelves." And then in Q1 we had another quarter where we doubled where we went from like four to eight.

like whoa okay now we're talking and if it became time to launch in the US uh we hired Patrick and Evan who joined from a competitor and we sort of had our first

boots on the ground in the US and then we felt like okay what we have is like a winning formula so we just need to crunch it out everywhere and now I think we're at another interesting point in

time where we've built all these different tools but the paradigm from now onwards is humans are are probably not going to work with all these tools

like basically agents will leverage the tools that we built. So I remember early when MCP came uh our CTO basically went well now Lora has two users it's human

users and agent users and every new feature that we build has to be able to uh cater to both and now we're seeing more people like basically use our agent that uses the tabular grid or like our

agent who uses our word editing capabilities than humans actually going and using those features at all. Cha

made a cool point to me recently, which is that um because you have we were talking about how you know companies that are preai and companies that are just fully AI native just have to be

built differently in various ways and the fact that you didn't build a preAI company I think gives you sort of like you know this unshackled mind to like you're not even trying to think about

some past alternative you're just like given what's in front of me what should a company look like and you know you talked about how like having YC see founders inside the company has been helpful and I'm sure there's like a lot

there but I'm curious about like what are like the main tenants that you've observed you know cuz now you've probably hired a lot of people who did you know work and built companies preai what do you think are like the main

tenants ideas cultural concepts that have been important to you just to like make it work in like a fully AI native world >> yeah so I think this idea that Chaitan brought up around um like you have to be

willing to like kill the stuff that you've done in the past is very important because I think in in more traditional software you had to build the foundations and then you build the stuff on top of it and you sort of kept

building the stack and in that world it was also very good to have like a technical architecture where one feature would rely on the same microservices as like other features but the problem is

in AI like maybe that feature now needs to scale really really quickly and the cost of writing software is so low that it's basically better to build your own

stack for like each thing and now that we hire you know finance professionals or even like lawyers internally to Lora or we just hired our first like tax person >> I think they come with a set of ideas of

like oh this is how I used to do it in my old company and everybody's forced to relearn I think and also question what their value is on top of the

general model capabilities in a way which is like very painful totally Brett Brett Taylor talked about this on this podcast too basically that like people are going to build something and 6 months later we might just kill that thing and everybody needs to be

comfortable with that which I think historically would be a lot of painful internal conversations so like do you have to change is that like a different culture for people >> I think it's a different culture completely I mean I think the culture is

you don't maximize for your function like you maximize for the company always and I'm very upfront with every exec who

joins Lora that in a way like you're joining with an expiration date and you have to continuously prove that you like scale out of that in a way because the

company is scaling so exponentially and I don't know if it was Mark Zuckerberg or somebody talked about like let's hire people with high y slopes and not like high y intercepts. I think about that a

lot mostly because I've had to do that right like I did not join or start Legora with a lot of experience but I've proven that at every new point in time I've scaled with the business and so

other people in the Gora needs to do the same and I think that goes for every function and you know I think an engineering team that's shipping the amount that we do previously had to be

like 500 people and now we can get away with being 50 and I think there's even a question of like Do we need to be more

than 100 engineers or is the bottleneck here really knowing what to build and building it in the right way and designing an experience that works for

you know hundreds of thousands of people that we now have on the platform. I

think the paradigm is like shifting all the time. What's nice about our work is

the time. What's nice about our work is that engineering is sort of a a road map of what's going to happen in other industries too. Like I think the general

industries too. Like I think the general models have come the furthest in coding but also those organizations are very quick to adopt and shift and so like engineering orgs are today looking slightly different and I think we can

expect the same in you know legal organizations.

>> Two things you brought up that you should it would be great if you could dive into. one is Lora doesn't really

dive into. one is Lora doesn't really have a long-term road map like you guys react >> and build today and you know when you

first got started you had this like nearly weekly cadence where that's how long you would road map to and these days it feels like you almost road map on a daily cadence and so like things

change tomorrow like you wake up and it's like we have to do something different talk about that lack of roadmap and then also the other thing that you've invested heavily in is just understanding model capability and the

sort of proprietary eval infrastructure you've built where you've had these conversations with the foundation model companies of how you're able to identify latent model capabilities that they

themselves are not aware of. I mean on road map like way back every new model just like unlocked new things right and when we got early access to GPD like 4.5

and you just realize that holy [ __ ] like now it can finally draft like an end to end thing and we don't need like all these harnesses and like things around it that's amazing like let's unleash it in a way that that works

>> by the way to do that you need sort of like a low ego organization cuz you build all this IP and all thisware and you're like, "Okay, now the model can do it."

it." >> Delete it all.

>> We worked really hard for 6 months.

>> Yeah.

>> We're deleting everything.

>> Yeah. It's incredible.

>> But I think a lot of the things that we have built, we know that we're going to delete someday.

>> And I guess you kind of need people to opt into that at the front end for that culture to really work.

>> We've also talked about it as like if we were if we're here today and we start building for the future that's over here, like that's too far out. Like our

customers are not going to adopt that.

They don't understand it yet. So we need to take them on the journey and we need to take them on the path of being successful which I think every iteration cycle now is shorter like back in 2023

2024 I think it was like slightly longer like you'd have a quarter or two quarters because the models weren't moving that fast every upgrade was pretty incremental but now it like

flipped Opus 4.6 flipped uh in capabilities so now we have to revisit a lot of the things that we built. Do you

know what the next flip you're waiting for is? Like is there a thing?

for is? Like is there a thing?

>> So, actually I don't think so. So So it was funny. I was at the customer

was funny. I was at the customer advisory board at Anthropic yesterday, which is I'm wearing my my like Dario shirt here.

>> You look like Dario.

>> Thank you. Most of that conversation was about the models are now intelligent enough where they're no longer the bottleneck.

The bottleneck is all of the software around putting the models in an environment where they can execute and do work and humans can review that work in like a trustworthy way. They're

seeing that across basically every single vertical and every single company. So I don't really think that

company. So I don't really think that we're waiting for new model capabilities anymore. There's like nice things to

anymore. There's like nice things to have like it's nice to have better context windows. It allows us to, you

context windows. It allows us to, you know, do less garbage and context management or or like when you overflow the context in in memory and so on, you have to like deal with it to refresh it.

So there's nice to haves, but I think we're at a point now where we just have so much building in front of us in in terms of bringing the model capabilities into our world that that's where all of

our focus is. I think on on sort of discovering what the models can do. We

thought very early on that eval were going to be important and both building up like an exercise of building new evals but also building out evals for all the use cases that we want to cover for because in the beginning it was a

lot of like oh how's good is how good is set how good is Gemini how good is GPT and so we had to test them on the different evals and a lot of our

customers um actually contributed this so they would give us manual tasks that they used to do and they tell us here's the evals and we're going to call you

when we can get to 100% on these evals.

And I actually remember it was a funds related use case, an LPA um key term review report that a Danish law firm was spending like three three

days on. Basically like an associate

days on. Basically like an associate would spend three days putting together that report. In summer of 2024, we had

that report. In summer of 2024, we had like 60% accuracy on that task. By the

end of that summer, we had 100% accuracy. And once you got to 100%

accuracy. And once you got to 100% accuracy, I mean that task is done. Like

it's it's over. I've adopted this mentality internally that if AI can do something, it will do it. And so our product like we think a lot about

solving legal tasks end to end and once a task is conquered, it's done. Like we

just like strike it out. And we're on this path of solving more and more complex tasks. like you start with NDAs,

complex tasks. like you start with NDAs, but at some point you get to full-on share purchase agreements which are very complex, but we're going to get there. I

think the question for for these organizations who are maybe more traditional and trying to keep up with the pace of AI is how do you do that while at the same time do your normal

job, right? I think a lot of the

job, right? I think a lot of the organizations that we work with really struggle with keeping up with the technology uplift even like our developments. And so we're, you know,

developments. And so we're, you know, we're struggling by getting all the latest models and then we're turning that into product and then they have to adopt it and then they're customers and it's Yeah. This is a question I think

it's Yeah. This is a question I think for both of you as I'm listening to you talk. I'm sort of like, you know, I can

talk. I'm sort of like, you know, I can sort of see the, you know, the hill climb that you're on where you've like tacked, you know, one part of it and the next one's coming and the next one's coming. And, you know, one of the things

coming. And, you know, one of the things I'm thinking about is for, let's say, a new startup in legal, what would the right strategy be for them? like how do you possibly get into the mix fast

enough or all of these things and >> exit to LGora?

>> Exit to sell to Lagora. That's a good one. How urgent is it to grow really big

one. How urgent is it to grow really big really fast for Lora given all of the dynamics around Chathan? I'm curious

like how do you think about this? Like

is it the same urgency as always or do any of these dynamics mean that like getting to real scale is more urgent here than other places?

>> We can go back to sort of launch day October 2024. So when when they launched

October 2024. So when when they launched you know roughly the AR of the business was rounded to a million dollars. If you

just go back into that moment you know there was this exercise of should we make a budget and you know what what we all decided around the table was there was no reason to make a budget because

we don't know anything about the market.

We don't know if people even like our product. We had instincts, but like we

product. We had instincts, but like we just needed to go literally as fast as we could to get the product as many hands as we could because ultimately the whole theory of the company didn't work

until we got product feedback. And so

that was literally the aim. The aim was like get this out as quickly as possible in as many hands as possible. And I

think one of the things that Max did, again, this was like it's cliche to say it's first principles thinking, but it is because it's like the team was

unbiased by how to build a software company. And so one of the things that

company. And so one of the things that you learned in SAS was the way you do pilots is like you would go in do like a time trial pilot where you would like give them access to the application and

the minute the the trial was done you would turn it off and then then they would have to make a purchasing decision.

>> A big thing that happened with Lora is they would go put Lagora into your organization and whatever you put into Lora they would like leave behind even if you didn't want it. M

>> and so there was this idea that like hey you adopted AI you did stuff with AI you built some practices but you're not like whatever skills you built or whatever IP you built it's kind of yours and like we

can leave that behind it's not a big deal it's like it's your skills it's your things that you learned and then Max went around and and just gave people like 30-day pilot 60-day pilot whatever they wanted 90day >> pilots and they would run these

competitive pilots right so they would say okay there's a couple of companies on the market we're going to want AB test all of them because it's really hard to pick just, you know, based on

the feature set on your website. And in

those pilots, I think we did an extraordinarily good job of delivering value. And so when the sort of 30 days

value. And so when the sort of 30 days were up, like if we shut it down, it would be a riot, >> right? Like people would roar and they'd

>> right? Like people would roar and they'd be like, "We've never seen software adoption like this in a legal organization. Um, we need this and we,

organization. Um, we need this and we, you know, we need it now." And in those pilots, we would demonstrate much better than any other company um the value that the product and the service around the

product could bring. So we hired all these lawyers um who are now called legal engineers. It's a great term I

legal engineers. It's a great term I think forward deployed legal.

>> I was going to say what about FDLE?

>> FDLE. That's right. FDLE. And they're

amazing. Like they're the most tech-savvy lawyers in different organizations who don't want to make partner because like you know that's one type of life and they want to work in a tech company and now they get to work

with their practice that they're amazing at and technology and then they get to work with the best legal organizations in the world and like driving that change >> and I would think once you're embedded in these organizations it's got to be

sticky.

>> I think Lori is very sticky. We've

ripped out our competition at many organizations at this point.

>> What creates stickiness? So the

stickiness is the uh use cases and the cadence and you know if you've invested time in building up a workflow that like works for you why would you want to switch? So is it that is it

switch? So is it that is it >> is it it's usage stick not no not yet >> not not any real technical implementation which is great because uh

our competition has been deployed in a lot of places that sees no real usage or very simple use cases which means that

we can go there show them and display clearly in a pilot that we deliver much much better and then we can easily swap it. So we actually have a dedicated like

it. So we actually have a dedicated like migration team moving deployments over to Luora.

>> But I think this is where we often talked about not only product engineering velocity which came naturally to the founders here because they were engineers but also this idea

of velocity of customer interaction which was like if a customer wanted to buy a certain way, wanted to do a pilot whatever.

>> Just don't add friction. That was

actually the key unlock which was like there was this idea of let's just go get this in everybody's hands and not to have any bias. And so one of my favorite

stories about Max is that he came to San Francisco to sell a bunch of clients and then you know he texted me and he was like are you free for dinner? So we met for dinner and then he asked for a ride

to the airport and I casually asked where are you going? Expecting to say Seattle or LA or something and he was like I'm going to New Delhi. And I was like, why are you going to New Delhi? He

was like, well, like one of the largest firms in India wants to buy. So I

figured I'd like go give it to him.

>> That's crazy.

>> And so you know this in SAS it was like no like do the west regional.

>> Yeah. then do like the east regional, then do like Western Europe, >> and then eventually hire an APAC head, and then then it was like this thing.

Yeah. And because this company didn't, >> by the way, there's going to be like a year of engineering work to be even kind of ready to serve India.

>> And because like this company and this team had never built a preAI software company, >> they didn't know they weren't supposed to go sell in India >> early, like one quarter into like

selling the product. So Max got on a flight went to India and the customer in India bought. So it was one of those

India bought. So it was one of those things where like because they didn't have the patterns >> they were able to get big globally in parallel.

>> You know what I also wonder on this? We

talked about this a little bit that like um being a Europebased company means that you are multinational from the beginning.

>> You have to.

>> And I think this I'm sure some of this is preai and I think it I mean a lot of it is. I also think there's a thing

it is. I also think there's a thing where like if you started in Europe, you've already learned how to sell to 10 countries and you know that there's differences in the way that the cultures

work and the way they purchase software and what the rules are and the regulations and these things.

>> Yeah.

>> And so I'm curious if you thought about that when you invested and you're like, well actually maybe coming to the US will be easier one day. I'm curious your experience on that. But like

>> I mean why cominator weren't particularly excited about backing a company in Sweden.

>> Yeah. Um, I remember the first interview with Gustav and he and he's Swedish, like a Swedish partner at YC. And he

goes, "Uh, so you're going to move to the States, right?" And I go, "Yes, yes, of course." Like that that's the cue to

of course." Like that that's the cue to say yes. So that you get the invite to

say yes. So that you get the invite to go to YC.

>> Now I'm good in Sweden.

>> Yeah.

>> You guys are opening Sweden. And then I came to YC and I left 3 days later because I had so much business going on in Sweden and I couldn't do work between 1:00 a.m. and 10:00 a.m. Like that was

1:00 a.m. and 10:00 a.m. Like that was just impossible. But you know, yeah,

just impossible. But you know, yeah, like the Swedish legal market is smaller than Kirkland and Ellis, so of course you have to expand. And naturally, we went to Finland and then we went to

Denmark. Then I was like, well, I think

Denmark. Then I was like, well, I think we got the hang of it. And the most important thing was like the first customer we got Manheimmer Futling, the big firm in Sweden, their managing partner had such a good relationship

with the other firms in other non-competitive countries that he would just introduce me and I would fly down and I'd say the same thing as I told him like AI is going to change the world and

you're going to need a partner. I'm

here. Let's work. You know, that sort of made it made it all start. But then the the move to the UK and the US was like when we really started ripping.

>> How different was coming to the US versus going to Finland?

>> No, no. I I I had a rule. So there's

actually a few Swedish companies that tried to go to the US but did so unsuccessfully like CLA. They tried many times before they like actually made it work. And my rule was if we can serve

work. And my rule was if we can serve two of the biggest clients in the world or in the US uh from Stockholm then we're ready and then we'll open an

office here. So clearly got like amazing

office here. So clearly got like amazing Wall Street firm and Goodwin and Proctor and we served them both. We won their business in competitive pilots and we

could serve them from Sweden. Like we

did a lot of flights back and forth. Um

but after they signed we said, "Okay, amazing. Now we're ready. Let's open an

amazing. Now we're ready. Let's open an office here." So, one thing about the

office here." So, one thing about the market structure of legal that we knew about here at Benchmark ahead of

investing is that legal has this unique market feature that it's a services industry. And in services industries,

industry. And in services industries, technology adoption is slow at first and then rapid later. So if you just look at any marketplace idea in a services area,

it's like the marketplaces are usually like supply constrained and then the minute supply unlocks all of the supply comes online into the market >> and then you become demand constraint.

And so if you study marketplaces especially marketplaces around services, this is something that you just like >> fundamentally learn as like one of the rules of marketplaces. And so in legal

the market structure is such that like the initial adoption will be very slow and hard but once it unlocks it like really unlocks there's some kind of like exponential viral coefficient that happens there. That's one part about the

happens there. That's one part about the legal industry that's really interesting and then how it overlays into software in legal is that if you look at the most successful legal software

companies they were all started in Europe preai too by the way. I had a hypothesis that part of the reason why you get that way is that you're used to

selling the multi- geography and multi-ruule systems from day zero. So,

for example, Lora sold to a Swedish firm.

>> Yeah, that makes sense.

>> And a Spanish firm and a Finnish firm.

And so, yes, they're like laws at the European Union level.

>> Yeah. But like from the beginning, you're like, "This needs to work for many people."

many people." >> That's right. And if you start in the US, what you end up designing is that there's the federal >> legal system, there's a state legal system, and then there's regional. But

it's not as bifurcated as like literally different countries >> and different languages >> in different languages. And so you build all this stuff on day zero that you

don't if you start in San Francisco. And

one of the interesting things that Max showed us in the prototype in the first meeting is he had multi- language support already built and he had like multi-leal framework support already built.

>> I remember I demoed Sweden and Spain.

>> That's right. And that was like remarkably impressive because it was it was a company with five people thinking global scale.

>> Yeah.

>> Because they were forced to because they couldn't serve the Stockholm legal market.

>> Totally. Those two things just meant from they launched the product, they got a bunch of people to sign that immediately it was like let's go get the two big firms in every geography because we have to

>> and it was global from day one >> and now I mean I think has become a a hub in like a technology hub in Europe like people from Germany from the

Netherlands from Spain from Italy they're all moving to Stockholm even in the winter >> to come and work with us. talk about the culture part of it which I think stands out a lot and so it's hard to describe

to people what it's like to visit the >> when you came back from a Lora visit recently you were like oh my god they are so good like something's going on there that I haven't seen before it sounded different I don't know if it's

Lora specific or if it's like something that is you know happens in Sweden that can't happen in America but like you were affected by it >> it's true initially even in the group of

five or in that group of 10 in September of 2024 24 or group of 15, however the however big the company was, there was like a a common thread amongst everybody. They were like deeply

everybody. They were like deeply technical, deeply intense and a desire to win. And they were like thinking

to win. And they were like thinking globally from like day zero. And because

they were in Stockholm, they also decided to recruit all over Europe from day zero to bring people to the Stockholm office. And so what ended up

Stockholm office. And so what ended up happening is I think you end up becoming a magnet for anybody that wants to build at the forefront of AI with a level of intensity and determination. This idea

of like wanting to win.

>> But so like what did it feel like to you on your recent trip? There's like a few hundred people in there. Like what did that feel like? The level of engagement and buyin

to the company mission was truly unique and I think the company has done a great job with this idea of building for the

company and I really do think building an AI company is like a real test in ego.

>> It's like you literally can't have an ego because you have to have this idea that AI is just going to do this. It's

going to be better than us at everything at some point >> and they're going to it's just going to do this. The foundation model will do

do this. The foundation model will do this capability and I'm like puzzling through this and it's really hard and it's an amazing feature and you know we have these high bars of quality and

polish. So, we're gonna like ship fast,

polish. So, we're gonna like ship fast, work really hard, build this amazing feature, and it's going to disappear within 12 weeks. Requires an extreme

amount of buyin and an extreme amount of humility that like we're just riding this massive wave and we don't know where it's taking us, but like every day we solve today's problems and we don't worry about tomorrow because it's a

different world. There's a different

different world. There's a different type of energy, buyin, cadence that comes with that culture. And I think that it's really interesting. The

disadvantage of Stockholm has now become Loro's advantage of being in Stockholm, which is that their talent population that they that they get to hire from is not just in

Stockholm. It's all over Europe. And now

Stockholm. It's all over Europe. And now

it's like all over the world >> because anybody that has that attitude is welcome to come join Stockholm. I

think our competition is, you know, has remote days, like three days in office, everybody lives at 6. Like from from

very early on, like we serve dinner at 8 every day. A lot of people in in our

every day. A lot of people in in our region are sort of tired about like all these big American winners and we know

that we have the talent and the grit and the prerequisites to build a generational company. Like yeah, we have

generational company. Like yeah, we have to go to the US to raise money because we want to work with the best VCs in the world, but like there is a a a level of like we can also do it, right? And we

have Spotify just on the street. How are

you going to get this level of fervor in the US?

>> I think we have I think we have a very unique culture in our New York office.

>> Is it different or is it >> very different like people Well, it's not different from Stockholm.

>> Oh, okay. But it's

>> we seated it with you know the culture carriers from Sweden that came to New York and I >> I think tactically this was a really cool thing they did which was I think like you should tactically talk about how

>> you make everybody interview in Stockholm.

>> Yeah.

>> And then they have to onboard in Stockholm.

>> Everybody on board in Stockholm. live in

New York, you're going to join the New York office, you're going to Stockholm, >> on board in Stockholm.

>> People who join in Sydney have to go on a 24-hour flight to on board in Stockholm.

>> So, you can't on board anywhere else but Stockholm.

>> And then when they first opened the first international office, which was New York, and actually London, too, you did this with which is like people that were based in Stockholm.

>> Yes, he did.

>> To those offices to set a cadence, which is like it's all going to be the same as Stockholm. And like the Germans who join

Stockholm. And like the Germans who join Luora like they have to move to Stockholm and they'll work here a year and then they can move back to open the the German office. Like you have to get it right.

>> It's a fascinating thing where you know I've been part of many companies that have many offices >> and every office tends to take its own character and I remember the founders of

Lora saying we want every office to feel the same which was itself a different way of thinking. Every time Max has had me visit the company, I visit during

dinner time, which is 8:00 p.m. Like

that's when they have guests is like 8:00 p.m. And that's been the case in

8:00 p.m. And that's been the case in every office. And so that's another like

every office. And so that's another like thing that happened at this company and it it's interesting to me that it continues to scale, which is like you can continue to onboard in Stockholm

because like every the 400 people that joined before you onboarded in Stockholm, >> so you should too. I mean, the only reason why we have that rule was because I did an internship at McKenzie and we'd have dinner at it. So, I was like, I

guess that's how you do this.

>> Totally. It's also like um in some ways doing doing the US office in New York.

Obviously, it's not like it's some outsider city, but from a tech perspective, there's a lot of people in New York like I want to work at like a great tech company. Yeah.

>> And you know, it's there's obviously been more there than in Stockholm, but it's still different than San Francisco.

And so, I think you could probably bring some of that cultural thing there as a result of that. Now we just opened in Houston and we're opening in Chicago.

It's all the big legal hubs. Is this

correct? Did you do a reference with Daniel?

>> Yeah. And I I think I heard this from you. You asked him about like what is

you. You asked him about like what is the culture at Leia?

>> And I think he said something like they're pretty intense.

>> That's right. We were very upfront with that even in interviews and not intense to the point where it's like not fun.

But um like coming like showing up as number like being number two in this space is like not a outcome worth fighting for. Like then we might as well

fighting for. Like then we might as well go do something else. Like we're only going to play here to win.

>> You think number one, number two will just be vastly different outcomes.

>> Oh yeah, completely. And it doesn't actually matter if that's the case or not, but that's >> gives you the right mind gives you the right mindset.

>> Yeah. Like I think everybody's dialed into that and I mean I remember doing this interview in Swedish and there's there's a saying um that you like like

blue smok and you like you taste the like you taste the blood and and because you you worked so hard and I basically told her in Swedish that yeah you know sometimes I I wake up and you know it's

a Swedish saying so it made sense coffee I tasted blood and and then and then she publishes the article in English. Yeah. And the saying

doesn't make any sense in English. Max

is no. It's like the Lora founders make wake up at Lagora. We wake up with a metallic taste of blood in our mouths and people in the company go, "Holy [ __ ] is Max a vampire or does he just floss badly?" Like,

floss badly?" Like, >> and what's going on?

>> And what how do you feel about that now?

>> Now it's become this thing like the Americans are #blmock. Like it's

everybody's in on it. It's amazing.

>> It's a cult I mean it it's I can like feel the energy of it. It's not like a culture that I think would quite work in San Francisco. Like I don't know if

San Francisco. Like I don't know if that's something that you can do uniquely.

>> Well, when we open our San Francisco office, they're going to taste the blood.

>> Yeah, they're going to taste the blood.

>> I love it. All right. My last question is you just raised a big round, which is awesome. Congrats. What does this mean

awesome. Congrats. What does this mean for the future? What's coming?

>> Well, maybe first off, just to give you a bit of insight into the round. Like

every round at Lora since Chathan has been a preempted round. I don't think I've ever actually gone out to like fundra since since you did the round.

Yeah, it's been it's been very pleasant.

It's been very pleasant. We actually

also have a history of taking the like lowest term sheets. Um I remember taking like we were actually so this is funny like we were negotiating the number of

shares that Chathan was going to buy on Excel in front of us and he goes I've never ever bought a company where I didn't get 20%. And I go, "Well, I'm never going to dilute more than 17.5."

And we sort of look at each other and go, "Well, I guess we're in a in a bit of a stalemate here." It's like the immovable object meets the force.

>> And so we like just put on Excel. We

write down the exact number of shares and we start going decimal by decimal.

>> Wow.

>> Until we're both That's such that is so legal coded. Like just the nerdy Excel.

legal coded. Like just the nerdy Excel.

>> It's It was wild. Perfect. So, you end up investing like 9.521 and we're both equally unhappy or happy.

I think we were both happy. That's a

good way to start. Of course, we're both happy. But the series D has been really

happy. But the series D has been really great because it's the first time I I've done it uh together with with uh someone else. So, David, our CFO, who just

else. So, David, our CFO, who just joined from Vanta, he's an absolute monster. It was funny. We had our our

monster. It was funny. We had our our companywide kickoff and you get to like pick the song you want to walk out to.

And he goes, "Max, I want Monster by Kanye West." And I go, "Okay, dude." And

Kanye West." And I go, "Okay, dude." And

it's like the lights drop and I'm like, "I have a big surprise for you everyone." Like David is joining us, our

everyone." Like David is joining us, our CFO. And like the speakers just explode

CFO. And like the speakers just explode with this like And I don't know if you heard the song. Yeah, of course. And

it's like starts and everyone's like, "Holy [ __ ] what's going on?" and he like comes up on stage and he's just like so much energy and in the references people refer to him as a CF

go and I was like that's amazing. So he

and I did the round. It was super fun.

It was the first time we went out to actually do a fundra. We had a we had a deck this time and it was wildly oversubscribed. I think we ended

wildly oversubscribed. I think we ended up having like 1.5 billion in demand for the round.

>> It's wild.

>> It was crazy. But we're super thrilled about Excel coming in and leading it.

some great participation from Menllo and Bane. You know, we're

Bane. You know, we're >> well, it's awesome. It's a huge testament to what you've done and um super exciting and I think you're you're just getting started. So, Max, thank you for doing this. Jathan, thank you as well and really enjoyed it.

>> Thank you so much, Jack.