Thomson Reuters just launched Deep Research—an AI system that doesn't just search legal databases, but plans and strategizes like an experienced attorney. In this episode, we explore how one of the world's largest legal research companies is using AI agents to transform how lawyers work, the challenges of building AI for high-stakes legal decisions, and what this means for the future of knowledge work. CTO Joel Hron shares insights from testing with 1,200+ customers, tackling hallucination risks in legal settings, and building professional-grade AI systems.Resources mentioned: Thomson Reuters Deep Research: https://www.prnewswire.com/news-releases/thomson-reuters-launches-cocounsel-legal-transforming-legal-work-with-agentic-ai-and-deep-research-302521761.html Westlaw & KeyCite: https://legal.thomsonreuters.com/en/products/westlaw/keycite Claude Code for development: https://www.anthropic.com/claude-code LinkedIn: Joel HronThomson Reuters Medium blog: https://medium.com/tr-labs-ml-engineering-blog Subscribe to The Neuron newsletter: https://theneuron.ai
Full Episode
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?
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