Sacra estimates Harvey hit $30M in annual recurring revenue (ARR) in August 2024, up from $10M at the end of 2023.

Harvey generates revenue through enterprise partnerships with major law firms and professional services companies. Key customers include Allen & Overy, where over 3,500 lawyers have used the platform, and PwC, which partnered with Harvey to develop custom AI models for tax, legal and HR functions.

The company recently expanded accessibility by launching on Microsoft Azure Marketplace, offering more affordable versions of its services to a broader range of legal professionals.

The company's revenue model combines custom implementation fees for large enterprise clients with recurring subscription revenue from its AI-powered legal tools.

Harvey currently holds a $1.5B valuation as of its latest funding round, with backing from prominent investors including OpenAI and GV (Google Ventures).

Based on 2023 data, Harvey had a 71.5x revenue multiple, derived from $10M in revenue against a $715M valuation at that time.

The company has raised $206M in total funding to date, with significant participation from leading venture capital firms including Sequoia Capital and Kleiner Perkins. Other notable strategic investors include Elad Gil and SV Angel.

Harvey was founded in 2022 by Gabriel Pereyra and Winston Weinberg, becoming the first company built on top of OpenAI's GPT-4 model specifically for legal applications.

Harvey found product-market fit as a specialized legal AI platform for large law firms, with Allen & Overy becoming their first major enterprise customer after 3,500 of their lawyers successfully tested the platform with 40,000 queries during day-to-day work.

The product functions as an AI assistant trained on both general legal data and a firm's specific work products and templates. Lawyers can use Harvey to draft documents, analyze contracts, conduct legal research, and identify issues across multiple practice areas and languages. For example, during due diligence, lawyers can ask Harvey to review contracts and flag potential risks or compliance issues.

Harvey's platform includes specialized models for specific types of legal work, accessible through three main components: a legal assistant for direct queries and drafting, workflow automation for routine tasks, and virtual data rooms for document analysis. The system is designed to maintain client confidentiality and privilege while integrating with existing law firm processes.

The company has since expanded its partnerships to include PwC and Ashurst, making the platform available to thousands of legal professionals globally through Microsoft's Azure marketplace.

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 seat annually with a minimum commitment of 100 seats, while custom model development for large law firms can exceed $5 million.

The company's core product suite includes AI-powered legal research, document analysis, contract review, and due diligence tools built on top of OpenAI's GPT models but specifically trained on legal data. Harvey differentiates itself by offering firm-specific model training using each client's work products and templates, similar to how new lawyers are trained when joining a firm.

Harvey employs a land-and-expand strategy, starting with core legal workflows and gradually expanding into specialized use cases. The company recently launched a more accessible offering on Microsoft Azure, providing legal assistant capabilities and workflow automation tools to smaller firms. This multi-tier approach allows Harvey to capture both enterprise clients through highly customized solutions while also serving the broader legal market through standardized offerings. The company plans to further expand its revenue streams through commercial access to specialized models for case law research and practice-specific applications.

Harvey operates in the emerging legal AI market, which includes both established legal technology providers and new AI-focused entrants.

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. These platforms focus on legal research, document analysis, and case law interpretation using specialized AI models. Unlike Harvey's custom-trained models for specific firms, most competitors offer standardized 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.

Large language model providers like OpenAI (ChatGPT), Anthropic (Claude), and Microsoft (Azure OpenAI) offer capabilities that can be adapted for legal work. While these platforms have sophisticated AI technology, they lack the legal-specific training and enterprise security features that Harvey provides. Some law firms are attempting to build their own solutions on top of these platforms.

The market is seeing rapid consolidation, exemplified by Thomson Reuters' acquisition of Casetext for $650M in 2023. Major law firms are increasingly partnering with AI providers, as demonstrated by Allen & Overy's exclusive partnership with Harvey and PwC's strategic alliance for custom AI models in tax and legal services.

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.

Valuation trap: Harvey's $715M valuation at $10M revenue (71x multiple) creates intense pressure to achieve unrealistic growth targets. The company will need to reach ~$280M revenue within 2 years to justify a Series C at traditional multiples. This could force premature scaling and excessive cash burn before product-market fit is fully established.

Custom implementation challenges: Harvey's need to train models on each law firm's proprietary documents and workflows limits scalability. The high-touch deployment model requires significant resources per client and extends time-to-value. This could constrain growth velocity and unit economics, particularly as they attempt to expand beyond elite law firms.

OpenAI dependency: As a company built on GPT-4, Harvey is heavily dependent on OpenAI's infrastructure and pricing. Changes to OpenAI's terms, costs, or competitive stance could materially impact Harvey's margins and competitive position. Their focus on custom implementations makes it difficult to quickly switch to alternative LLM providers if needed.