Harvey and EngramLab have released an open-source synthetic dataset containing more than 100 million tokens, creating a fake law firm’s entire body of work for training AI systems. The dataset covers over 250 synthetic client matters across 46 clients, with roughly 10,000 individual files representing institutional knowledge typically held by senior partners. EngramLab’s contribution centers on its memory-layer technology, which the company claims can compress organizational context to cut token usage by up to 100x. Earlier in 2026, Harvey open-sourced the Legal Agent Benchmark, known as LAB, which established standardized ways to measure AI agent performance on legal tasks. Harvey is currently valued at $11 billion. EngramLab raised $98 million in funding on June 23, 2026, at a valuation of approximately $600 million. The partnership between the two companies predates this dataset release, with the Legal Agent Benchmark serving as an early public output of their collaboration. Law firms face barriers to assembling training data due to attorney-client privilege, work-product doctrine, and competitive secrecy, making synthetic data a workaround that offers structural complexity without confidentiality concerns. EngramLab’s 100x compression claim could address bottlenecks in deploying AI at scale within law firms, as a single M&A transaction might produce tens of thousands of pages of documents. For Harvey, the open-source strategy reinforces its position as an infrastructure layer for legal AI.