The company invested $40 million to train the model, utilizing a foundation of open-source architecture refined through decades of proprietary data from Westlaw, Practical Law, and Reuters. Unlike general-purpose models that prioritize raw scale, Thomson is specifically engineered for domain-specific reasoning and complex instruction following. CTO Joel Hron noted that the strategy focuses on deep specialization rather than brute-force compute, enabling the firm to maintain full control over the model's behavior and data privacy.

Early performance evaluations indicate the model is competitive with top-tier frontier alternatives, particularly in legal and tax-related tasks. The company has begun integrating the technology into CoCounsel Legal, specifically for tabular document analysis. To facilitate external validation, Thomson Reuters is releasing a smaller, open-weight version of the model on Hugging Face for academic and non-commercial research. This shift toward in-house development marks a strategic move to secure AI sovereignty, allowing the firm to offer professional tools that meet strict fiduciary-grade standards for accuracy and accountability.