Harvey: How a Legal Tech Startup Built a $5 Billion Business
🔥 An inside look at Harvey's go-to-market strategy 🔥
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AI-native companies are re-writing the GTM playbook. Every other week, I will highlight the stories and frameworks behind some of today’s fastest growing startups. You can expect deep dives, analysis and insights to inspire the next generation of AI-native founders and operators.
Today, we take a look at Harvey.
Year Founded: 2022
Headquarters: San Francisco, CA
Total Funding: $806M
Founders: Winston Weinberg, Gabe Pereyra
Let’s dive in 👇
The Making of a Legal AI Giant
In the summer of 2022, Winston Weinberg was working as a litigator at O'Melveny & Myers, one of America's most prestigious law firms. His colleague, Gabriel "Gabe" Pereyra, had just left his AI research role at DeepMind. When Weinberg shared his daily legal workflows with Pereyra, and Pereyra introduced him to the capabilities of GPT-3, something clicked.
Three years later, their company Harvey AI serves over 300 enterprise clients globally, including most of the top 10 U.S. law firms and major corporations like PwC and KKR. The company's annual recurring revenue hit $75 million by April 2025, with projections to exceed $100 million within months.
A few weeks ago, Harvey raised $300 million in Series E funding at a $5 billion valuation. The round was co-led by Kleiner Perkins and Coatue, with participation from existing investors, including Conviction, Elad Gil, OpenAI Startup Fund, and Sequoia. The financing comes just four months after Harvey announced that Sequoia led a $300 million Series D round at a $3 billion valuation.
Harvey's meteoric rise offers a masterclass in how to bring cutting-edge technology to traditionally conservative industries. The story reveals hard-won lessons about building trust, the power of domain expertise, and why sometimes the best go-to-market strategies are the ones that don't scale—at least not at first.
Finding Product-Market Fit in an Unlikely Place
Before Harvey became a household name in legal circles, the founders tested their hypothesis in the most practical way possible: they turned to Reddit. Scouring forums like r/law and r/legaladvice, they collected landlord-tenant law questions and fed them to GPT-3 using early chain-of-thought prompting techniques.
The results were already promising. When practicing landlord-tenant attorneys evaluated the AI-generated answers, they approved them 86% of the time. This was before most lawyers had even heard of ChatGPT, let alone used it professionally.
Armed with these results, the founders made a bold move: they cold-emailed Jason Kwon, then General Counsel at OpenAI. That email led to a July 4, 2022 meeting with OpenAI's executive team, where Kwon reportedly expressed surprise at how well the models performed on legal tasks. This early validation became the foundation for a strategic partnership that included investment from the OpenAI Startup Fund.
The lesson here wasn't just about the technology—it was about finding the right problem to solve. As CEO Winston Weinberg later explained, "The messiness of real-world problems" in law created an opportunity for specialized AI that generic models couldn't address. Legal work involves complex, nuanced tasks using specialized datasets often not publicly available. In Weinberg's words: "Process data for a lot of these tasks doesn't exist on the internet."
The Trust-First Strategy That Changed Everything
Harvey's early go-to-market strategy defied conventional startup wisdom. Instead of trying to scale quickly across many smaller customers, the company focused obsessively on winning over a select few of the world's most prestigious law firms.
"If you earn the trust of a few of those firms, the rest of them will trust you and the rest of the firms downstream will definitely trust you," Weinberg explained. This "Prestige & Trust" approach meant investing in tactics that, as Weinberg admitted, "do not scale at all."
The company created hyper-personalized demonstrations using prospective clients' own recent work. They would analyze a law firm's actual cases and show exactly how Harvey could have helped with those specific matters. Even more unconventionally, they encouraged lawyers to "fight with the model" during demos—challenging the AI's responses and testing its limits.
This approach worked because it understood the legal profession's DNA. Lawyers are trained to be skeptical, argumentative, and detail-oriented. By letting them directly challenge the technology, Harvey transformed potential skepticism into active engagement. Even when the AI's responses weren't perfect, the process of critiquing them demonstrated the tool's potential value.
Allen & Overy (now A&O Shearman) became Harvey's watershed moment. The global law firm conducted an extensive trial where 3,500 attorneys asked 40,000 questions before committing to a broader rollout. This wasn't just a customer win—it was a signal to the entire legal industry that AI had arrived.
The strategy paid off handsomely. Harvey's first 50 enterprise customers were all referrals from law firm clients, creating a powerful cascade effect where prestigious early adopters opened doors to their corporate clients.
Building AI That Actually Works for Lawyers
While many early AI applications were essentially "GPT wrappers"—thin layers of functionality over base models—Harvey took a fundamentally different approach. The company invested heavily in what they call "process data": the step-by-step procedures for complex legal work that experienced practitioners know but that isn't documented anywhere online.
Consider executing a leveraged buyout (LBO). The process involves intricate, often unwritten procedures typically learned through years of practice. Harvey systematically hired experienced lawyers to map out these processes—"these are the steps that I would take"—creating a unique, proprietary dataset about how legal work actually gets done.
This focus on domain-specific expertise runs throughout the company. Harvey employs around 50 lawyers in product design, evaluation, and customer-facing roles. These aren't just consultants—they're integral to how the company builds and sells its products.
The technical architecture reflects this specialization. Harvey uses a multi-model approach, incorporating LLMs from OpenAI, Anthropic, and Google, then fine-tuning them for legal tasks. The company has developed its own benchmarks, like "BigLaw Bench," because generic AI benchmarks don't capture the nuances of sophisticated legal work.
The product strategy follows what Weinberg calls "Expand and Collapse." First, the company builds highly specific workflows for particular complex tasks—like M&A compliance analysis or antitrust filing requirements. Then it "collapses" these capabilities into a simple, unified interface that looks like familiar email or chat but is powered by sophisticated underlying orchestration.
This dual approach lets Harvey tackle deep, valuable problems while maintaining broad usability—crucial for a seat-based business model where the platform needs to serve many different types of users.
Disrupting the Billable Hour Model
Harvey's business model evolution reveals sophisticated thinking about value creation in professional services. The company started with traditional seat-based SaaS subscriptions but increasingly focuses on "selling the work" itself through revenue-sharing agreements.
Here's why this matters: If AI makes lawyers dramatically more efficient, they logically bill fewer hours for the same tasks. In an industry built on billable hours, pure efficiency can actually reduce revenue unless firms can capture more market share or fundamentally change their pricing structures.
Harvey's solution involves co-building specific workflow solutions with law firms and splitting the revenue when those firms sell new AI-enhanced services. For example, Harvey might help a firm automate side letter compliance for private equity clients—work often done at a loss to secure more lucrative deals. With Harvey's technology, firms can offer these services profitably on a flat-fee basis, sharing revenue with Harvey.
This approach aligns incentives across the entire value chain: Harvey, the law firm, and the end client all benefit from the AI-generated value. As Weinberg puts it, "efficiency is a hard sell" unless firms can "transform their business."
Scaling Without Losing the Magic
By 2024, Harvey faced a classic scale-up challenge: how to move beyond founder-led, bespoke customer acquisition while maintaining quality and trust.
The company systematically built scalable pipeline generation engines. It hired Sales Development Representatives, established formal content marketing programs, and invested in enterprise-grade sales technology including Salesforce with CPQ capabilities, Salesloft for sales engagement, and Gong for conversation intelligence.
Strategic partnerships became increasingly important. The company's relationship with Microsoft extends beyond using Azure for infrastructure to developing integrations with Copilot and Microsoft 365. Partnerships with consulting firms like PwC—which is both a client and a referral source—help expand market reach.
Harvey also created specialized roles for different customer segments and geographies. The company now has distinct Enterprise Account Executives, Mid-Market Account Executives, and specialized Business Development Leads for specific regions or verticals like tax services.
The hiring of foundational roles like Harvey's "first" Content Marketing Manager and a Product Marketing Manager tasked with "establishing the function" signals the formalization of marketing operations. A dedicated GTM Systems Lead now architects and manages the entire sales and marketing technology stack.
Navigating a Competitive Landscape
Harvey operates in an increasingly crowded legal AI market. Traditional players like Thomson Reuters (with CoCounsel) and LexisNexis (with Protégé AI) are integrating AI into their existing platforms. Specialized competitors include Eve (focused on plaintiff firms), Spellbook (contract drafting), Robin AI (contract review), and Alexi (litigation support).
Harvey differentiates itself through deep domain expertise integration, complex workflow automation, and strong relationships with elite firms. The company's multi-model approach and proprietary process data create defensible advantages that pure technology alone can't replicate.
Interestingly, LexisNexis has invested in Harvey through its venture arm REV, creating a "coopetition" dynamic where companies compete for market share while potentially collaborating on ecosystem integration.
Lessons for Other Industries
Harvey's trajectory offers several key insights for founders targeting conservative, high-stakes industries:
Trust Trumps Scale in Conservative Markets: Harvey's early focus on prestigious clients, even through unscalable methods, built invaluable credibility that cascaded throughout the market. Sometimes the best go-to-market strategies are deliberately unscalable at first.
Domain Expertise as a Moat: Hiring and integrating subject matter experts throughout the organization—from product development to sales—creates genuine differentiation and builds customer trust in ways that pure technology cannot.
Evolve Business Models with Customer Value: Harvey's move beyond pure SaaS to revenue-sharing models shows sophisticated thinking about aligning business models with actual value creation, especially in industries with entrenched economic structures.
Build Defensive Technology Strategies: The multi-model approach, proprietary process data, and custom benchmarks create barriers that pure platform plays struggle to replicate quickly.
Plan for Systematic Scale: While early unscalable tactics may be necessary, successful companies proactively build the operational infrastructure needed for systematic growth.
The Road Ahead
Harvey now serves clients across 42 countries. The company's employee count has grown from approximately 40 at the start of 2024 to around 577 by May 2025 with plans to keep growing the team with the recent round of funding. Some of the new staff will be hired to help Harvey build AI products for professional services beyond legal, including tax accounting.
Challenges remain. Customer retention has been identified as a key focus, and the company must continue differentiating itself in an increasingly competitive market. The shift toward outcome-based pricing models requires more complex sales processes and deeper strategic relationships with clients.
But Harvey's foundation appears solid. The company culture emphasizes rapid execution and continuous improvement. Its strong financial backing, clear vision for transforming professional services, and proven ability to build trust in conservative markets position it well for continued growth.
For the broader AI industry, Harvey demonstrates that the most valuable applications may not be general-purpose tools but deeply specialized solutions that understand the messy complexity of real-world professional work. In an era when many AI companies chase broad horizontal markets, Harvey's vertical focus offers a different path to building defensible, valuable businesses.
The legal industry, traditionally resistant to change, has embraced Harvey because the company didn't just build better technology—it built better solutions to actual problems lawyers face every day. That human-centered approach to AI may be the most important lesson of all.
This analysis is based on public information, interviews, and company materials as of June 2025. Harvey AI's rapid evolution means some details may have changed since publication.
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