Thomson Reuters has developed its own AI model, named Thomson, specifically for legal, tax, and compliance work. This model was trained using the company's extensive proprietary content, including data from Westlaw, Practical Law, Checkpoint, and Reuters. The development involved an investment of approximately $40 million, covering compute resources and talent.
Instead of building a foundation model from scratch, Thomson Reuters started with an existing open-source foundation. The majority of the investment focused on further training the model with decades of proprietary content and expert-driven evaluation. This strategy offers an alternative for companies with large datasets, allowing them to create specialized AI without the multi-billion dollar cost of developing a new foundation model.
The Thomson AI model is designed to power specific features within Thomson Reuters products. For example, it will be the default model for Tabular Analysis in CoCounsel Legal, a feature capable of processing up to 10,000 documents and answering 100 questions. This capability is aimed at legal, tax, and compliance professionals who use these products daily.
Despite developing its own model, Thomson Reuters continues to utilize frontier models from other providers. Its new CoCounsel Legal product, for instance, is built on Anthropic's Claude Agent SDK. This indicates a hybrid approach where specialized in-house models handle specific tasks, while external models are integrated for broader functionalities.
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Thomson Reuters has developed its own AI model, named Thomson, for legal, tax, and compliance tasks, trained on its proprietary content. This model will power specific features within products like CoCounsel, while the company continues to use third-party models like Anthropic's Claude for other functionalities. This approach allows companies with extensive proprietary data to create specialized AI without building a foundation model from scratch.