Sacra estimates that Harvey hit $195M in annual recurring revenue (ARR) in 2025, up 3.9x from $50M at the end of 2024.

Harvey crossed $100M ARR in August 2025, roughly three years after founding. The company's technology is now used by some 100,000 lawyers across 1,300 organizations, including the majority of the AmLaw 100, 500+ in-house legal teams, and 50 asset managers. Weekly active users grew fourfold year over year and monthly queries grew 5.5x, with active files reaching 9.75M.

Revenue is seat-based: accounts typically start with a few hundred licenses for research, drafting and diligence, and internal usage data show that median seat count doubles within 12 months. Sector-specific modules for insurance and financial services, along with integrations into document-management systems, have lifted average contract values.

In March 2026, Harvey closed a $200M growth round at an $11B valuation, co-led by GIC and Sequoia Capital.

In December 2025, Harvey raised $160M led by Andreessen Horowitz at an $8B valuation, with participation from WndrCo and accounts advised by T. Rowe Price, alongside existing backers including Sequoia Capital, Kleiner Perkins, Conviction, and Elad Gil. Harvey also completed its first tender offer as part of that transaction.

In October 2025, EQT Growth made a €50M ($59M) strategic investment earmarked for international expansion. Harvey previously completed a $300M Series E at a $5B valuation in June 2025, co-led by Kleiner Perkins and Coatue, just four months after raising a $300M Series D at a $3B valuation led by Sequoia in February 2025. Total capital raised exceeds $1.2B.

Harvey was founded in 2022 by Winston Weinberg, a former securities and antitrust litigator at O'Melveny & Myers, and Gabriel Pereyra, previously a research scientist at DeepMind and Meta AI.

Harvey found product-market fit as a legal AI copilot for large law firms and corporate legal departments, offering specialized capabilities for document analysis, legal research, and multi-language translation. The platform has surpassed 1,300 customers across 60 countries, with operations spanning offices in San Francisco, New York, Toronto, and Bengaluru.

The platform helps lawyers analyze complex legal documents, conduct research across case law databases, and draft legal documents. When reviewing contracts or legal filings, Harvey can identify key provisions, flag potential issues, and generate summaries. For legal research, it can analyze precedents and relevant cases, helping attorneys build stronger arguments and identify potential weaknesses in their positions.

The company scrapped its proprietary vertical model after frontier reasoning models from Google, xAI, OpenAI, and Anthropic began outperforming Harvey's custom legal model on its own BigLaw Bench evaluation. Harvey now positions itself around pre-configured agentic workflows that chain multiple LLMs and tools together to complete specific legal tasks, with the system handling 400K+ agentic queries per day, 25,000+ custom workflows built by users, and 445K+ reports generated from Deep Analysis. Customers can route tasks to Anthropic Claude and Google Gemini alongside existing OpenAI systems via a Model Selector, and firms can construct their own task-specific agents through Agent Builder—a no-code tool layered on top of Harvey's platform (launched March 2026).

Harvey has deepened its legal content layer through a strategic alliance with LexisNexis, integrating statutes, case law, and Shepard's citations directly into the platform through co-developed workflows that initially cover motions to dismiss and summary judgment. The platform's reach extends further through a Microsoft 365 Copilot integration (launching Q2 2026), allowing lawyers to invoke Harvey inside Copilot for agreement analysis, research, negotiation support, and precedent retrieval.

Harvey has expanded beyond its core legal offering through strategic M&A. The company acquired Hexus (January 2026), a San Francisco company that builds AI tools for creating product demos, videos, and guides, to accelerate its products for in-house legal teams, and added the Lume AI team in a second talent-focused acquisition aimed at strengthening its integrations work.

Harvey is a subscription SaaS company that licenses customized large language models (LLMs) to law firms and corporate legal departments, with pricing based on both per-seat licensing and custom model development fees.

The company's base offering starts at $1,200 per lawyer per month with 12-month commitments and roughly 20-seat minimums.

Harvey's model combines software licensing with intensive "forward-deployed" services—Harvey dedicates roughly 10% of its team to ex-lawyers in customer success roles who drive change management, implementation, and adoption within law firms to ensure clients hit utilization thresholds needed for renewal.

The shift from custom model training to pre-configured agentic workflows reduces Harvey's implementation complexity while maintaining the high-touch service model that justifies premium pricing. This approach allows Harvey to scale more efficiently across different legal use cases without the resource-intensive custom training that previously limited growth velocity.

The legal AI landscape has fundamentally shifted as frontier reasoning models have commoditized legal reasoning as a core differentiator. Major LLM providers including Google Gemini, xAI Grok, OpenAI, and Anthropic now match or exceed specialized legal models on standardized benchmarks, forcing vertical AI companies to compete on workflow orchestration and enterprise integration rather than model performance.

This commoditization favors companies with strong distribution and implementation capabilities over those relying primarily on proprietary model training.

The most direct competitors are specialized legal AI platforms built for large law firms and enterprises. These include Casetext's CoCounsel (acquired by Thomson Reuters), ROSS Intelligence, and Blue J Legal. Swedish startup Legora represents fast-growing competition in the enterprise legal AI space, having reached a $1.8B valuation. These platforms focus on legal research, document analysis, and case law interpretation using specialized AI models.

New startups have emerged targeting specific legal niches within Harvey's broader legal workflow platform, potentially fragmenting the market by offering specialized solutions for particular practice areas. EvenUp builds tools for personal injury lawyers, Finch focuses on paralegals, and Supio addresses plaintiff law. These vertical specialists compete for specific use cases that might otherwise fall within Harvey's domain.

Large language model companies like OpenAI, Anthropic, and Google are eager to expand products that reduce scutwork for white-collar professionals, exemplified by Claude Code's popularity competing with AI programming startups like Cursor and Cognition. Anthropic has moved into direct competition with Harvey by launching a legal plugin to help users with legal tasks. While these platforms lack the legal-specific workflows and enterprise security features that Harvey provides, their underlying model capabilities make it easier for law firms to build custom solutions.

Established players like Thomson Reuters (Westlaw), LexisNexis, and Wolters Kluwer are integrating AI capabilities into their existing legal research and practice management platforms. These companies have massive databases of legal content and established relationships with law firms, but their AI offerings tend to be more limited in scope compared to pure-play AI companies. LexisNexis has moved toward co-opetition by entering a strategic alliance with Harvey rather than competing purely on its own AI stack, integrating its statutes, case law, and Shepard's citations directly into Harvey's platform. The market is seeing rapid consolidation, exemplified by Thomson Reuters' acquisition of Casetext for $650M in 2023.

Harvey has tailwinds from the rapid advancement of LLM technology and growing enterprise acceptance of AI tools, with opportunities to expand beyond legal services into the broader professional services market and eventually become an AI super-app for knowledge workers.

The legal services market represents a $300B+ initial opportunity in the U.S. alone. Harvey's early success with firms like Allen & Overy and PwC demonstrates the massive efficiency gains possible through AI-assisted legal work.

The company's ability to train custom models on firms' proprietary documents while maintaining security and compliance creates strong competitive moats.

Harvey's text-processing capabilities naturally extend to adjacent professional services markets like accounting, consulting, and financial services. These industries face similar challenges around document analysis, research, and compliance.

The global professional services market exceeds $5 trillion annually, presenting an enormous expansion opportunity as Harvey develops industry-specific models and workflows.

The ultimate vision for Harvey extends beyond specialized professional services to become a general AI super-app for knowledge workers. By starting with high-value, compliance-sensitive use cases in legal, Harvey is building the enterprise-grade infrastructure and security protocols needed to serve broader knowledge work applications.

This positions them to capture a significant share of the global knowledge worker productivity market, estimated at over $50 trillion annually. Their partnership with Microsoft Azure provides a scalable distribution channel to reach this broader market.

Multi-model coordination complexity: Harvey's pivot to agentic workflows that chain multiple LLMs—now including OpenAI, Anthropic Claude, and Google Gemini—creates operational risks around model coordination, latency management, and cost optimization across providers. Managing reliability when workflows depend on multiple external AI services introduces additional failure points and makes debugging more complex than single-model deployments.

Custom implementation challenges: Harvey's need to configure workflows on each law firm's proprietary documents and processes limits scalability. The high-touch deployment model requires significant resources per client and extends time-to-value, which could constrain growth velocity and unit economics as the company expands beyond elite law firms.

Foundation model provider encroachment: Harvey's orchestration layer faces structural commoditization pressure as OpenAI, Anthropic, and Google build increasingly capable legal-specific products directly—Anthropic has already launched a competing legal plugin. As the underlying models improve, the differentiation of Harvey's workflow layer narrows, raising the risk that large firms build comparable solutions in-house or source them from foundation model providers directly.