Harvey

“I think there's constantly a debate around like, well, won't, you know, chat GBT enterprise, just eat up all of this market. I don't think that's the case at all. I think that to build a great vertical AI company, like a Decagon, a Sierra, or Harvey, you have …”

“Literally in the very first meeting, when we talked with him, he said, I need help with my storytelling. Like I know my product crushes. I I'm like, there's no ends, ifs or buts about that. How do I get that out there into the world?”

“Yeah, I mean, I would say in Europe, I think our win rate is, like, over 70% so I don't think it's as much dominant there.”

“So in this one, I think it's around, like, 350, and then New York is around, like, 300, something like that.”

“Our DAU over MAU at the beginning of the year was around like 36%. Right now it's like 51, 52%.”

“Our fastest growing of like the, of the verticals in general is financial services is number one for sure. So like banks, private equity, asset management and then pharma is actually growing pretty fast too.”

“If you are making an acquisition, like, the number one thing you should be looking at is just talent. Because you can build things so much faster now, right? That it should literally just be talent.”

“In this company we have over 200. Yeah. And so, and then on the commercial side, like, actually, I guess doing what a normal lawyer would do there's only about, like, 25.”

“Normally, you know, companies need to, like, reinvent themselves maybe every, like, five years, 10 years, something like that. I think you have to do it every six months.”

“I think every single company is going to sell intelligence. Like that is going to be the core of the company.”

“Yeah, 42% is in-house corporates. It's growing faster than the law firms, technically”

“And then this year, most recently, the main switch we did is just went over to cloud agents. Like we switched our entire infrastructure to that. And once we did that, usage literally just started, like, doubling quarter over quarter.”

“Our token usage in January was one trillion. Like for the month of January, and this month it'll probably be like 12 or 13 trillion.”

“And so with that, we've now actually created basically like a pipeline for creating synthetic data sets across like every single legal use case.”

“We're around 900 folks 950 people, 960, something like that. We're at around 2000 customers somewhere around three hundred million ARR.”

“No, we actually, we haven't used a lot of that money.”

“So a lot of what we're building is essentially this taxonomy of here's all the tasks that a law firm would do, and here's all the subtasks that associates would do, and how partners or senior associates would grade that work.”

“I think for some of the labs, we are like the largest consumer of embeddings.”

“And I think our approach is a bit more inspired by the DeepMind one, just because I think because we're in a vertical, the end goal is very clear.”

“We are going to start publishing some of our research that we're doing with the labs or the other providers and open sourcing more models, more of the work we're doing.”

“We have simple like assistant type queries where you say, you know, draft me a document that a single query can cost 20 dollars. We have like a review product where you can upload a 100,000 contracts and ask the models to review them, and some of those can cos…”

“There's customers like us, where we do need to move some of our traffic to open source models because it's just becoming super expensive to serve like the largest closed frontier model.”

“I think we're just starting to see in the past six months that inflection for legal where the models can now generate entire documents and they're starting to work in a way where Most lawyers who aren't using this technology just for fun, they're like, this ne…”

“We think of our strategy as we want to open source the general stuff and work with all of the providers to make these models as good as possible at general legal. And then we want to build infrastructure for these law firms and enterprises that help them own t…”

“What we've seen is actually every different model is good at something different. And so with the initial results we saw, Anthropics models are quite strong, but there's areas where a 5.5 is better. There's some areas where open source is better.”