Legora CTO: Why AI Agents Need More Than a Chat Window

Jacob Lauritzen (0:00)

Let's say you're getting sued, and there's multiple strategies and multiple ways that you could handle this case. That requires a lot of risk judgment, a lot of client collaboration, and I'm sure if you go to different partners at different law firms, they will probably all give you a different answer. If there's no objective truth, it should be the human who makes that judgment.

Simon Maple (0:17)

The question I ask there is just why? Why doesn't the AI just choose one?

Jacob Lauritzen (0:20)

Let's say I'm a client and I'm trying to get this thing done, trying to just go with whatever AI says, yeah, you probably could.

Simon Maple (0:28)

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Hello and welcome to another episode of The AI Native Dev. My name is Simon Maple. I'm your host for the day and joining me is Jacob Lauritzen, who is the CTO of Legora. Jacob, welcome to the episode. How are you?

Jacob Lauritzen (2:01)

I am doing great. Thanks for having me.

Simon Maple (2:03)

It's a pleasure. Jacob, tell us a little bit about yourself and also, for those who haven't heard of Legora, tell us a little bit about what you do.

Jacob Lauritzen (2:12)

My name is Jacob. I'm the CTO at Legora. I've been with Legora for about two years, a bit more.

Legora is a sort of workspace product platform for legal work. So we help lawyers basically do all of their stuff much faster, much better with AI. So it's sort of analogous to, in our world, GitHub linear cursor all in one. That's Legora for lawyers.

Simon Maple (2:39)

Amazing. And yeah, a lot of people who maybe haven't dived into the legal AI side of things may not be familiar. So it's great to hear that perspective. One thing for those who haven't heard is that Legora have hit 100 million ARR across 18 months.

Is that around correct? I didn't misquote there.

Jacob Lauritzen (3:03)

No, you're right.

It took us 18 months to get to 100 million. And last week, we just announced 200 million. And that took us six months.

Simon Maple (3:10)

Wow, congratulations. It's amazing when companies do this. What does it look like from the perspective of the engine, the head of engineering, the CTO?

Jacob Lauritzen (3:22)

I mean, I think it's just always exciting that people get value from the product that we're building.

That's sort of the thing for us, right? And then it's, you know, you can imagine a company scaling so quickly as we're scaling that it's a little bit chaotic. Absolutely. A lot of things are happening, a lot of traffic. We have so much usage now, but it's super exciting. I mean, everyone's super thrilled.

Simon Maple (3:44)

Amazing. And let's look a little bit under the covers, because obviously that kind of like speed of growth from a company requires huge flexibility and speed from the point of view of the engineering team, the company underneath. And we'd love to jump into some of the AI practices that you use.

From what I understand, AI writes over half of Legora's code base and I hear...

Jacob Lauritzen (4:07)

I think all of it.

Simon Maple (4:09)

Oh, pretty much all of it. Wow.

Jacob Lauritzen (4:11)

Yeah, yeah, yeah, yeah.

Simon Maple (4:12)

Now, I heard some engineers can run 10 concurrent agents at the same time. First of all, I'd love to understand how quickly did your team get to such a level, because I've played with two or three and it does take time to actually go beyond a couple or a few.