We are working with @EngramLab to train models that can understand a law firm’s knowledge. A huge source of differentiation for a law firm comes from all the past work the firm has done. When an associate is working on a new deal they have access to decades of similar deals the firm has done and a big part of the job is knowing how to effectively leverage that knowledge. However, a large amount of this data is either client data or derived from client data which means you can’t naively train all of it into a single model because this would mean breaking confidentially. In order to separate the research problem from the data privacy and deployment problem we built a synthetic law firm. @ItsJulioPereyra wrote an awesome article describing the dataset, tasks and approach to creating a synthetic version of the type of data you would expect to find at a large law firm. The firm has 46 clients, 266 client matters and each client matter contains work product, versions, emails, drafts, etc that you might find when searching the DMS of a firm. This dataset allows us to explore how well agents can reason across large corpuses of knowledge (100M+ tokens) and require complicated multi-hop reasoning. For example, answering a query like “in similar deals, how did we structure the reps and warranties” requires the model to understand what makes deals similar and then search over all past deals to find examples. We find that generic agent approaches for this type of reasoning are both expensive and not exhaustive and there is a lot of room for improvement. Excited to open source this dataset and also share more results soon

Was great chatting with @oneill_c and @mudithj about some of the research we've been doing together and broader AI topics

Q2 recap for @harvey - +$100M NNARR - 53% DAU/MAU Key hires (including Q1) - Anique (CPO) - prev VP of Product at Rippling - Rachel (CMO) - prev CMO at Notion - Brooks (CISO) - prev CISO at Roblox - Keith (CSO) - prev CPO at Google Product - Agent unification - cloud agents can use all Harvey product surfaces - Command center (EA) - monitor adoption and ROI by use case - Contract intelligence (EA) - agentic contracting platform for enterprises Eng - Migration to cloud agent infrastructure - Integrating open source inference providers - Scaling document processing (54TB / week) AI - Legal Agent Bench - Open source post training - Published multiple research directions with partners We invested heavily in cloud agent infrastructure at the end of last year and in Q1. In Q2 we also unified many of our product surfaces (collapsed as @winstonweinberg says) by making them all tools accessible by our cloud agents. Prior to this, there were a lot of capabilities in Harvey that were often only discovered by power users. As cloud agents get better and our product becomes more connected we are seeing users discover more of the product by learning from their agents (see plot of product surfaces per user).