In just three years, Harvey AI has scaled from a legal tech experiment to one of the most closely watched AI companies in enterprise software. Founded in 2022 by Winston Weinberg, a former associate at O’Melveny, and Gabe Pereyra, a former DeepMind and Meta researcher, Harvey now stands at a $5 billion valuation following its recent $300 million Series E round.
The new round, co-led by Kleiner Perkins and Coatue, follows another $300 million Series D round led by Sequoia just four months prior. This is a dramatic funding cadence ($600 million in less than half a year).
Harvey’s valuation has grown from $3 billion to $5 billion in a span of four months, reflecting a 67× revenue multiple on its $75 million annualized run rate. These figures put Harvey well above legacy competitors in legal tech. Clio, for example, reached a $3 billion valuation in 2024 after 17 years in business. Harvey did it in under three.
CEO Weinberg attributes this pace to necessity. “If you expand as quickly as we are, you just need to do raises like this,” he told Fortune. The company plans to double its 340-person workforce, an unusually aggressive strategy, where AI startups typically keep headcount lean.
Harvey’s customer base now includes eight of the ten largest U.S. law firms, in-house legal departments at PwC and KKR, and a presence in 53 countries. As of June, the company had secured 337 legal clients, including Ashurst, CMS, and Vinson & Elkins.
Execution at Scale with Platform Integration
Much of Harvey’s traction stems from its decision to embed legal professionals directly into the product development cycle. The company has hired dozens of former attorneys to work alongside AI engineers, helping design workflows that align with real-world legal processes. “We have legal researchers who are former Big Law attorneys sitting alongside AI researchers and engineers,” said Niko Grupen, Harvey’s Head of Applied AI. “They whiteboard together from concept.”
This approach has helped Harvey mitigate one of the largest concerns in legal AI: trust. Outputs are evaluated not only for accuracy but also for tone, formatting, and contextual appropriateness. The firm’s proprietary “BigLaw Bench” evaluation tool benchmarks these qualities and has been critical in model selection. Harvey’s integration of Anthropic’s Claude model, for instance, was based on its strong performance in long-context reasoning: an essential requirement for document-heavy legal workflows.
Partnerships are another element of Harvey’s scale-focused strategy. A recent alliance with LexisNexis enables Harvey to provide citation-backed answers using the latter’s extensive legal database and Shepard’s Citations system. The companies are jointly developing generative AI-powered workflows for common legal motions such as dismissals and summary judgments.
Harvey’s ambitions go beyond legal. The company is expanding its platform to support adjacent professional services such as tax and accounting. This puts Harvey into a broader field of AI-powered enterprise platforms, competing not only with legal tech veterans like Ironclad and Clio but also adjacent newcomers such as Eudia and Supio, which have collectively raised hundreds of millions in the past year.
The Consequence of Speed
Rapid growth comes with execution risk. Harvey’s hiring spree defies conventional wisdom in AI, where many startups remain small to optimize capital efficiency. “The labor-intensive growth strategy carries execution risks,” notes recent Tech in Asia analysis, citing industry data that 75% of companies regret scaling AI talent too quickly.
There’s also competitive pressure from model developers. Critics have labeled Harvey a “GPT-wrapper,” a dismissive term used for startups perceived to merely repackage general-purpose language models. However, the company’s domain-specific tooling, enterprise-grade security, and structured agentic workflows suggest deeper integration than surface-level wrappers.
Still, as competitors like Clio, Casetext (now owned by Thomson Reuters), and Legora adapt or consolidate, the gap between AI-native platforms and traditional legal tech may widen. According to Crunchbase, 79% of legal tech funding since 2024 (almost $2.2 billion) has gone to AI-driven companies, further underscoring the urgency for incumbents to respond.
In the words of Kleiner Perkins partner Ilya Fushman, who co-led the Series E, Harvey “sets the blueprint for how a vertical AI enterprise company can build and execute.”
Yet questions remain. Can Harvey maintain this momentum? Will it succeed in expanding into tax and other verticals where the nuances of regulatory language differ?
Read More: Harvey AI Came Out of Nowhere and Took Over Legal Tech
For now, Harvey appears to be building for independence. Its integration of multiple foundation models, including Claude and OpenAI’s GPT series, allows for flexibility. The company also boasts enterprise-level features across its four main products, designed to meet compliance, customization, and data privacy standards at scale.
“We’re taking complex legal tasks and turning them into end-to-end, interactive journeys,” said Grupen. This human-in-the-loop approach, combined with high-caliber legal talent, may be Harvey’s strongest defense against obsolescence in a fast-moving sector.
