Harvey's Strategy--Transforming Law Firms Into AI-First Organizations

Key Quotes

"At Harvey we're building AI for law firms and large in-house teams. We're almost at a thousand customers, 500 employees, started about just over three and a half years ago and so been kind of scaling quickly since then."

Gabe Pereyra explains the rapid growth and customer base of Harvey, highlighting its significant scale and client adoption within a relatively short period. This quote establishes the company's substantial market presence and its focus on serving both law firms and large corporate legal departments.

"I'd say the past, the kind of first two years of the company were how do we build essentially the IDE for lawyers around these models that connect it to all of the context you need to be productive as an individual lawyer. But I would say in the past year and going forward the big problem we're solving is not how do you make individual lawyers more productive, it's how do you make a team of lawyers working on a client matter more productive and more importantly how do you make an entire law firm working on thousands of these client matters more productive and more profitable."

Pereyra articulates a key strategic shift for Harvey, moving from enhancing individual lawyer productivity to optimizing team and firm-wide efficiency and profitability. This demonstrates the company's evolution in addressing more complex, systemic challenges within the legal industry.

"And so the main legal and podcasts and podcasts which is actually important but there's less legal work there -- and the important things you need to do there are fund formation, so how do I structure the entity that is going to hold all that money and it sounds easy but if you're a large private equity firm you have a sovereign wealth fund that comes in and they say we need to structure it in this way because of tax implications then you have a pension fund that has these other requirements and so it ends up being this incredibly complex process of how do you draft the limited partnership agreement which can be 100 pages."

Pereyra uses fund formation as an example to illustrate the intricate workflows within large law firms, emphasizing the complexity beyond simple document drafting. This quote highlights the sophisticated legal structuring and coordination required for major financial transactions, showcasing the depth of problems Harvey aims to solve.

"And so a lot of the systems we're starting to build look a lot like that and I think one interesting direction that the coding labs, the research labs are going is building these RL environments where you can deploy these agents and they can interact with a codebase and see if they can pass unit tests and in legal that RL environment is a client matter so you have all of the context of a fund formation, an acquisition, a litigation and the models are starting to learn let me go into the document management system and see if I can find this go into the data room or do case law research get feedback from the partner."

Pereyra draws an analogy between coding environments and legal client matters to explain the application of Reinforcement Learning (RL) in legal AI. He describes how AI agents can navigate complex legal scenarios, akin to agents interacting with a codebase, to perform research and gather information within a client matter.

"I think the part I'm optimistic about is if I think about over 10 years ago when I learned to program it was super painful right like you had to go on Stack Overflow it was hard to learn multiple languages because you're like okay I'm just going to like learn Python I'm going to learn TensorFlow or something it was just like hard to even learn that and I think it's stuck all the time yeah exactly and now with the models it's like programming is so fun to learn because you can just be like here's how to write this in Python translate it why is it written this way and you can learn this so much more quickly and we see lawyers doing that with Harvey where they'll say generate this merger agreement why did we structure it that way."

Pereyra expresses optimism about how AI models are transforming learning and skill acquisition, comparing the current ease of learning programming with AI assistance to the more arduous process of learning a decade ago. He notes that lawyers are experiencing a similar acceleration in their learning and task execution through Harvey.

"The bigger point is for us it feels like the best outcome is if we can figure out how do we make every law firm how do we help every law firm become an AI-first law firm, not how do we build one ourselves and I think the real problem we're trying to solve is can we make every law firm more profitable and a part of that is how they work with their clients and can you make their clients get better faster cheaper legal services."

Pereyra clarifies Harvey's strategic focus, emphasizing their goal to empower all law firms to become AI-first entities rather than competing by building their own firm. He reiterates that Harvey's core mission is to enhance law firm profitability and improve client service delivery through AI integration.