How Thomson Reuters Built AI Agents That Think Like Lawyers
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Lawyers, what if you had a legal research tool that also helped you strategize? Thomson Reuters Deep Research does exactly that. And today, we talk to CTO Joel Rahn about it.
Welcome, humans, to the Neuron AI Explained. I'm Corey Knowles, and with me as always is Grant Harvey. Hello, hello. Today, we're going to talk to Joel Rahn, CTO at Thomson Reuters, about Deep Research, an AI agent platform built for legal strategy, not just document search. We'll unpack how it works, what testing with 1,200 customers taught them, the hallucination risks in legal settings, and what this means for lawyers and knowledge workers moving forward. Joel, welcome to the podcast.
Thank you guys for having me. Nice to see you, Corey and Granton.
Nice to see you too. We're sure excited to have you on here. We know there's been a lot of talk in the legal community and around AI and how people are using it and some of the benefits they're finding and some of the limitations they hit along the way. So we thought this was just a really good conversation to have and we're excited to have you here chatting with us.
Yeah, excited to talk about it. It's been a lot of great work from the teams over the last nine months or so to get here. So it's an exciting topic to talk about.
Yeah, so let's dive into it. So Thomson Reuters just launched deep research. Can you walk us through what, like, for example, I think a lot of our readers are familiar with, like, ChatGPT deep research. So perhaps you could walk us through, like, how this is different and how it kind of works. Like, for example, how is this different than, say, just putting all your docs into ChatGPT deep research? Why would they want to choose this instead?
Yeah, that's a good question. So there's a number of implementations of deep research today, as you mentioned, like ChachiBT, Perplexity, Claw, Gemini have a variant of this, and most of them really orient around research via the web, right? So they're tremendous at kind of navigating all the deep links of search and sort of reading and learning and taking notes along the trajectory of learning and doing more searches and Uh, as you think about, you know, let's say like a B2C use case of like planning a vacation, it's a very iterative thing. Like, uh, you know, I'm going over the summer and like, what are the options? I could go here. Like what's cheaper? Like what's more family friendly. And like, there's a ton of just like trajectories of like search that you would do for that problem, which is why deep research is, is really phenomenal.
Um, The same is true in the legal context, though, and in legal research context as well, in terms of the various different trajectories of research that could be taken. And it's actually even more complex in many ways because many aspects of the law, like, you know, say the same thing but in a different way or they might like overrule each other or the jurisdictional nuance you know might be particular to the context and where you are and sort of where the case is being tried and all this stuff and what judge is in front of you and all these things that kind of i think formulated from a human's perspective like how they would go do that research And so the sort of aspect of how the agent reasons through that process needs to be tuned for the domain of law.
And, you know, when I'm not a lawyer myself, but. you know, haven't worked with many for the last several years. Like, you know, you go to law school to learn like what the patterns of good research look like and what sort of the, you know, way to formulate an argument is. And there's precedent for how to formulate good arguments or how to like argue against arguments. And so there is a structure to how good label research should be done. And then the second part, and so that's all on the like AI reasoning side of the equation. The other side of the equation is like, okay, what tools and content and information does this agent have available to it to go do that research? And as Thomson Reuters, this is sort of one of the things that we've been exceptional at for many, many years.
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