Harvey
@harvey
AI for the world’s most complex legal work.
Joined March 2023
.@gabepereyra gave a talk to @sequoia founders about Harvey’s approach to research. Gabe shared our research playbook, including: 1) Building Legal Agent Bench, our open source benchmark 2) Leveraging the frontier ecosystem to scale post-training and experimentation with aWant world class research capabilities, but don’t have the resources of a big lab? At our recent Sovereign AI event, @gabepereyra shared @harvey ’s “moneyball” approach. Here’s the playbook: 00:00 Introduction 00:37 Building a research lab on a budget 02:28 Legal Agent Bench,- We post-trained @NVIDIAAI Nemotron 3.5 Lightning on Legal Agent Bench with @trajectorylabs. Here's what we found: 1) Post-training improved agent performance from 0% to 8.3% on held-out LAB tasks, beating both Opus 4.6 and the much larger post-trained Nemotron 3 Ultra. 2)Continual learning is a bet that the retraining loop will get cheaper over time. With larger models, you can maybe run this loop once every few weeks. But with smaller models, you can run it nightly, per customer. And it keeps recursing: a model per company, then a model per
- Jessica Escalera is the Chief AI Officer, Legal at @HSBC, one of the world's largest banking and financial services organizations. @GabrielMacht sat down with Jessica to discuss what becomes possible when legal teams can analyze documents and data at unprecedented scale.
- We're open sourcing a 100M+ token synthetic law firm we built with @EngramLab. The firm contains work product from 250+ synthetic matters across 46 clients, spanning ~10k files. We built this environment to evaluate an agents' ability to search and understand a firm's past
