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AI companies should selectively own their intelligence to address cost, latency, and data privacy constraints by using post-training and online learning to build continuously improving domain-specific models.
This paper compares a domain-trained small language model (Olava Extract) against frontier LLMs for structured contract extraction, showing that the specialized model achieves higher F1 scores and dramatically lower cost.