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

We are hiring for @Harvey’s model training team. This team will help Harvey expand from the application layer into the model layer and from legal into high end knowledge work more broadly. We are hiring AI researchers of all seniority, particularly those with experience post-training frontier or open source models. Our program is centered around large-scale model training, synthetic data generation, long horizon reinforcement learning, and rigorous evaluation in real world deployments. We are scaling-pilled and believe that nothing beats the combination of larger models and better training data. We’ve been able to generate incredibly realistic legal environments and validated that this allows us to post-train open source models to achieve frontier performance with agents. We plan to scale up these data generation and training efforts significantly across legal to start, and eventually other verticals. As a researcher, you will have access to thousands of GPUs and unique training data from our product and customer relationships. Your research will inform Harvey’s product strategy and power AI used for some of the most economically and societally impactful work in the world.

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).

Model strategy for @harvey: We are working on the first model in our legal foundation model series, inspired by @cursor_ai's Composer. Two goals: 1. Allow us to serve frontier intelligence across our product surface areas at an affordable price and a strong security posture. 2. Create the foundations for law firms to build their own specialized models and own their own intelligence. The model series will focus on complex client matters that span months and take dozens of associates. The agentic system will learn to control legal tech tools, sub agents and ask for help from frontier models or human partners, much like a senior associate. We’ve open sourced benchmarks for evaluating our initial post training work that represents work done by associates and in-house lawyers. We are scaling these significantly using synthetic and human pipelines as well as building private evals for firms. Open sourcing this data has allowed us to quickly validate the feasibility of post training open weight models for legal work. With our research partners we’ve already shown promising results post training open source models to approach frontier performance: 1. @baseten - novel compaction strategies for analyzing large data rooms. 2. @FireworksAI_HQ - matching frontier performance by using frontier as an advisor. 3. @appliedcompute - improving performance and reducing cost of large scale review tables. 4. @trajectorylabs & @nvidia - sovereign continual learning over client matters. We plan to continue to invest heavily in working with research partners and open sourcing our data, models and research as much as possible. We believe open research in legal will be important to building trust in the frontier ecosystem. We are also scaling our research team. Harvey Labs is our internal research group, responsible for pushing the frontier of legal intelligence and working closely with labs, research partners, and academia to bring the frontier of agent research into Harvey. Labs is run by @nikogrupen and @ItsJulioPereyra - Niko worked on multi-agent RL at Google Brain and Julio clerked and worked in BigLaw. We believe this pairing is crucial for building frontier legal AI systems. Together they have already made significant progress in scaling our data and training efforts. The long term goal of Harvey Labs is to contribute to the research and infrastructure required for the legal industry to create a frontier ecosystem. We believe that the best version of legal super intelligence is one where each law firm, enterprise and government owns their own specialized version. We are hiring for Harvey Labs across the post training, agent and data stack and open to acquiring talented teams / neolabs in this space. If interested please DM me.

I actually think this is directionally correct. Tech companies are starting to spend the same on tokens as they do on eng headcount. Other knowledge work industries will follow with some delay. Kirkland spends billions on lawyers so it’s not crazy to assume they will spend a similar amount on tokens in the future. Most companies are starting to realize they need to own their AI. As models get better law firms will need to better articulate their value to in-house teams using codex / claude for legal. Kirkland is sitting on one of the most valuable AI datasets in the world (all their associate and partner feedback and trajectories). The cost of replicating this dataset is essentially Kirkland’s yearly labor costs. I’m very bullish they can convert this into AI differentiation above the foundation models. There’s no need to build everything from scratch like a lot of the commentary assumes. The infrastructure for building / training / deploying agents has matured significantly. It’s very possible for a firm like Kirkland to use inference providers and rl-as-a-service companies to leverage their valuable data without needing to hire OpenAI level talent. We’ve already seen promising results doing this for similar firms and post training models on lawyer feedback. We think the best future for legal is one where every law firm is able to convert their unique expertise into their own AI and use that to provide their clients with exceptional service. We are starting to invest heavily in training our own models and helping firms do the same. Excited to see a top firm like Kirkland taking a big bet like this.