Why we built our own AI model: legal department perspective

Highlights

• Thomson Reuters launched its own AI model trained on proprietary legal content and expert validation.
• Thomson outperformed leading models on factuality, scoring 0.83 versus 0.65-0.68 for competitors.
• The model powers CoCounsel Legal's Tabular Analysis with no customer data training.

Every general counsel we talk to is fielding the same pitch right now: a faster model, a sharper wrapper, a new way to search the same case law you already have access to. It’s a crowded market, and most of it is built on the same handful of general-purpose models, wrapped in different interfaces.

We took a different approach. On August 24, we launched Thomson, our own large language model, trained on decades of Westlaw, Practical Law, Checkpoint, and Reuters content and validated by our own subject matter experts. It’s not a layer on top of someone else’s model. It’s a model we built, on content only we have, to a standard only we can set.

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The question that matters isn’t which model is smartest

What separates Thomson from a wrapper

What the results show

Where you’ll see it first

The part that should matter to your risk committee

The question that matters isn’t which model is smartest

General-purpose AI is optimized for breadth — it needs to write a poem, debug code, and summarize a contract equally well. That’s a reasonable design goal for a consumer product. It’s the wrong design goal for legal work.