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Thomson Reuters’ Deep Research uses multi‑agents on a 20 billion document corpus, cutting 20‑hour legal research to 10 minutes with direct citations for enterprise compliance

September 17, 2025 //  by Finnovate

Thomson Reuters Westlaw’s Deep Research platform was specifically designed to take its time, working an average of 10 minutes. This allows the multi-step research agent to plan, execute and pull from a deep, curated dataset of more than 20 billion docs — up-to-date case law, statutes, administrative rulings, secondary sources and structured legal editorial content. On the back end, it connects with a highly developed toolset that attorneys can use to check findings and probe deeper into legal scenarios. Deep Research is designed to eliminate errors and hallucinations, providing direct citations from Thomson Reuters’ vast dataset. The result is an AI agent that mirrors the rigor of human legal research, providing legal nuance and reducing the time attorneys spend on discovery. For enterprises beyond law, the system offers a blueprint for how AI can move past speed into substance, indicating that slowing AI down can provide real business value. Deep Research on CoCounsel is embedded into Westlaw, Thomson Reuters’ legal research platform used by 12,000-plus law firms, more than 4,000 corporate legal departments and the majority of the top U.S. courts and law firms. Typically, lawyers can spend 10 to 20 hours performing research for complex legal matters. While Westlaw doesn’t yet have definitive numbers, Deep Research is speeding up that time “dramatically,” while also surfacing relevant materials to advise their clients, produce better briefs and motions and litigate more effectively. While Deep Research’s default option is 10 minutes, there are seven-minute and three-minute versions available; the team is also working on a longer 20-minute version. While devs or researchers in the lab are often looking to make models faster and faster, lawyers aren’t seeking instant gratification; they actually prefer longer output options.

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Category: AI & Machine Economy, Innovation Topics

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