Harvey, DeepJudge team up to give AI agents institutional intelligence

Together, the providers said, “they enable AI agents that not only generate and evaluate high-quality outputs but also align them with what ‘good’ looks like within a specific legal team, grounded in institutional expertise, prior decisions, and the collective judgement built through years of practice”.

Speaking about the partnership, DeepJudge chief executive and co-founder Paulina Grnarova said legal AI has made remarkable progress on reasoning, and Harvey is a testament to that.

“DeepJudge brings past work, decisions, and institutional expertise directly into that reasoning, so that the resulting work reflects the judgement, standards, and ways of working unique to each firm or legal department,” she said.

“Together, DeepJudge and Harvey enable legal professionals to manage the full arc of legal work seamlessly, while ensuring AI outputs are grounded in how they actually practice.”

Harvey chief executive and co-founder Winston Weinberg added that DeepJudge knows your firm through every past matter, memo, and negotiated position: “Most firms have decades of expertise embedded across prior work and decisions, but that knowledge is often fragmented and difficult to apply consistently in practice.”

VIEW ALL

“This partnership closes that gap by bringing a firm’s institutional knowledge directly to Harvey users, enabling legal teams to ground their work in prior expertise and run their practice on a system that reflects how they actually operate.”

The partnership will address the “context tax” that limits the effectiveness of legal AI, the providers said: “While most organisations possess decades of institutional expertise, that knowledge is often fragmented, inaccessible, and difficult to apply consistently in real time, leaving even advanced AI systems without the context required to reflect how legal teams actually operate.”

In practice, the providers went on, legal teams can move seamlessly from research to drafting to decision making, informed by the full scope of their institutional knowledge. Teams can surface prior positions and accepted language directly within their workflows, draw on analyses from similar matters, and identify relevant materials across matters, teams, and industries with greater speed and precision.