Hello and welcome to the High Conviction podcast. Today's guest is Max Junestrand, the CEO and co-founder of Legora, an AI native legal operating system that lets lawyers draft, edit, and review documents in a fraction of the time. Max is 25 years old, a former competitive esports player turned engineer who studied computer science and machine learning at KTH and business at the Stockholm School of Economics with a stint at McKinsey before deciding to go all in on Legora. The results speak for themselves. Max and the team at Legora raised a $550 million series D at a 5.5 billion valuation. They crossed the 100 million ARR mark in under 18 months, making them one of the fastest companies ever to hit that milestone. Going through YC, they're now 400 people strong and powering more than a thousand legal teams across 50 markets, including White and Case, Kellar, and Goodwin. Well, hi Max. Welcome on the High Conviction podcast. Thank you so much for being here today. We're really looking forward to this chat.
Awesome. Could you first kind of give our listeners a core overview of what Legora is, the use cases and the problem that you're really solving for lawyers?
Sure. Well, look, Legora is an AI platform where both lawyers and agents do legal work together. We started back in 2023 and from the time that we started until today, not only has the underlying model intelligence moved a lot but also the sophistication and complexity of the use cases that we can solve and increasingly we've also been working with both law firms and enterprises to enable collaboration between different types of organizations and the company has scaled from a small startup in Europe to a global player with over 10 offices all over the world and things are scaling like a true rocket ship like you said before we started.
Yeah, I think I saw that the employees went from 40 to 400 in the last year. You guys truly are absolutely exploding. I mean, maybe if we can kind of go back to the beginning on a bit more personal side about you. You studied computer science in Sweden. You know, I can see the link of why you're so interested in AI, but maybe could you talk a little bit about why you got so interested in legal and looking now law and language models seems like a clear use case, but maybe at the time not so clear.
Sure. Well, Thomas, I actually did at two different universities in parallel. So, I did a degree in computer science and AI and then I did a degree in economics and business. And when I graduated, LLMs had just come in the form of ChatGPT and GPT 3.5 and their application area within law I think was quite apparent. It's very text-heavy work, low degree of digitalization prelims and so there was a lot of problems to go out and solve and then we just executed extraordinarily quick and at a very high level.
Certainly. I mean maybe going on to Legora a bit more. You touched on Legora wanting to be the operating system for all lawyers and where all legal work gets done. Just wanting to understand a little bit about the defensibility maybe specifically against the foundation layer. Would you say it's Legora's edge is in how deeply it's embedded in lawyers' existing workflows with the Outlook integration or would you kind of more say that it's the playbooks and the vertical specific features you've built on top?
Well, Thomas, I think it's probably a bit of all of it, right? The way that we deploy with large legal functions and law firms is that we sort of roll out firmwide and then we integrate both into their existing document stack with their legal research stack. We also integrate with their permissioning systems and the way that you can deploy agents in a sort of trust and governance high environment. And I think increasingly the model intelligence is just allowing us to do more things faster. We sort of certainly don't view the general platforms as competition but as enablers for the work that we do.
And I guess we kind of talked a little bit about the general platforms. Harvey seems to be the big competitor that you're up against in the legal AI space. Just looking at the kind of features you guys have, it seems you have pretty similar features. Could you just talk a bit about how I should see the differentiation between other legal AI players?
Well, I think there's a lot of legal AI players out there because of the good application areas of the models themselves and I think this is very good for the market. I think most of the differentiation comes from who do you want to work with and who do you think is going to have your best interests in mind and who's going to work hardest with you on building the next set of features that you need and making sure that the adoption and ROI becomes strong within your organization because the space is moving so fast. It's very difficult to take a static world view where you say okay features at this X or Y today but three months from now it might look very different. So at Legora one of the things that I've been very conscious of is how we're structured and organizationally set up in a way so that we can ship a lot of new things very quickly in an environment where trust and compliance and governance is sort of front and center.
Certainly. Yeah. And we mentioned a little bit about working with partners and the trust there. I thought it was super interesting the kind of competitive pilots that large law firms run and that's how they kind of select which AI software, legal AI software they want to use and you guys certainly over the last year have been extremely successful at winning most of those pilots. Could you maybe touch on what that process is like and why you think specifically in the last year you've been so successful?
Yeah. So I think it's a great thing by the organizations who are looking to embed AI in their legal organizations to evaluate multiple products and when they do they are testing the capability, the partnership, the pace and they're looking at how good of a long-term partner will a provider be. And what we do is we really put the client front and center. How do we make them as successful as possible and what can we do to unlock new use cases, new ROI within the different practice areas and with different professionals and that's also one of the big reasons why we pioneered the team around legal engineering so we actually have close to 100 lawyers on staff at Legora working proactively with our clients on getting best-in-class adoption and the sort of complex use case buildout.
You've got a kind of wide range of features at the moment covering Tableau View integrations in Outlook and Word. What would you say is the next kind of workflow or feature that you guys want to target or what's the direction that you're looking to kind of head in next?
The next features will be announced at our big event in London called President.
Gotcha. Okay. Well, we will be listening and waiting for that one. Just pass over to Jack for a couple.
Awesome. Thanks, Tom. It's been super interesting to hear about Legora's trajectory. I wanted to sort of pivot a bit and discuss the law firms themselves and the legal market more generally. Looking 5 to 10 years ahead, how do you see AI agents and LLMs reshaping the structure of law firms? Do you think platforms like Legora will take over much of the junior level work? And could that eventually push firms towards a more partner heavy model? How do you see that playing out?
Well, look, AI is going to be performing certain tasks at a very high degree and a very very high level. And when it's able to do so, the firms need to figure out how to use that capability to get a bigger market share and deliver better work for their clients faster with higher quality and to spend less time doing the things that AI will now do and more time doing the things that only humans can do. And AI will inevitably make the world spin a lot faster. So there's going to be a lot more work. And I think one thing that's sort of certainly true is the firms that try to keep the existing structure for a new world will likely not do as well as the people who look to adapt and really harness the technology and the change that that's bringing with it.
Yeah. When we spoke to Winston from Harvey, he mentioned that they've been pushing more into in-house legal teams. How do you think about that? Is the long-term opportunity still mainly in law firms or does it shift towards in-house?
We're getting inbound from all over the market and our ambition is to serve every lawyer and every type of legal task and that exists in many different places and so that would mean that we work with big enterprises like banks, insurance companies, governments but also big law firms, right? And I think innovation can happen in many different shapes and places, and we want to work with as many of those organizations and teams as possible.
And what's the split looking like now percentage-wise between law firms and other enterprises?
It's about a third in-house and two-thirds law firms.
Awesome. And just to pivot to pricing, how do you price the product? Is it seat-based, usage-based, and do you think that model will change over time?
So we have many different pricing models and I think the way that you price over time always has to be in relation to the value that you bring and the more and more value we bring and the more and more value organizations are getting out of our products, the closer we can sort of tie the two together. I think initially we've been very focused on how we can price in a way that's easy to buy and going forward it's a lot about sort of really aligning interests as well because when organizations start to embed AI they often find ROI being tracked to usage and I think that can take many different shapes and forms.
And building on that what's the single metric you track internally that tells you the level of engagement and whether the product is actually delivering value to your customers.
We track usage with our customers and we track the level of sophistication of things that they're doing with the platform.
So just jumping in, would you say that a kind of like DAU over MAU? So like daily active users over monthly active users. Is that the best metric for you for usage or maybe a couple different ones?
We have internal metrics that are more applicable to the work that we do around tracking the ROI and results that our clients are getting and that's what we're tracking more than simple stats around how many times you've logged in.
Gotcha. Gotcha.
Great. And I guess you sort of alluded to it before the nature of the legal industry is that the work they're doing is of such consequences. And I guess in most AI applications, the hallucination is inconvenient, but in the legal industry, it can create a really big liability. And so, how big of a barrier is that when you're selling to top law firms? And how do you sort of cultivate trust with them?
