Harvey
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Harvey is an artificial intelligence platform built for the legal industry, developed by Counsel AI Corporation and headquartered in San Francisco. Founded in 2022 by former litigator Winston Weinberg and former Meta AI researcher Gabriel Pereyra, Harvey trains and deploys custom large language models for law firms, corporate legal departments, and professional services organizations. By early 2026, Harvey had raised over $1 billion in total funding at an $11 billion valuation, making it one of the most richly valued legal technology companies in history. The platform serves more than 1,300 organizations across 60 countries, including a majority of the AmLaw 100, and counts OpenAI, Sequoia Capital, and Andreessen Horowitz among its investors.
The company takes its name from Harvey Specter, the fictional lawyer played by Gabriel Macht in the television series Suits. In February 2026, Harvey signed Macht as a brand ambassador.
Winston Weinberg grew up in New York and attended Kenyon College before earning a J.D. from the University of Southern California Gould School of Law. He joined O'Melveny & Myers as a first-year associate in the securities and antitrust litigation practice. During that time he began experimenting with OpenAI's GPT-3 language model, prompted by curiosity about whether generative text models could handle the kind of repetitive analytical work that fills a junior associate's day. He tested the model on tenant law questions he encountered in practice and, with some prompt engineering, found results that impressed him.
At the time, Weinberg was sharing an apartment in Los Angeles with Gabriel Pereyra, who had a PhD-level background in machine learning and had spent several years as a research scientist at Google DeepMind and later at Meta AI. Pereyra had published work on regularization techniques for neural networks and had a practical understanding of how large models fail, which would prove relevant when Harvey later began confronting the hallucination problem. The two began building in Weinberg's spare time after work.
Their first test was a set of 100 legal questions sourced from Reddit's r/legaladvice community. When they showed the model's responses to working attorneys, more than two-thirds said they would send the answers to clients without editing. That result convinced them the idea was commercially viable.
On July 4, 2022, they cold-emailed Sam Altman and OpenAI general counsel Jason Kwon. The pair met with OpenAI's senior leadership the same day and came away with a commitment from the OpenAI Startup Fund. The connection also gave them early access to GPT-4 before its public release, which proved to be a significant competitive advantage: GPT-4's improvement in reasoning, legal citation, and longer-context comprehension over GPT-3.5 was large enough that the demo quality jumped substantially overnight.
Harvey has raised capital through eight rounds since its founding in 2022, with each round coming at a materially higher valuation.
| Round | Date | Amount | Valuation | Lead investor(s) |
|---|---|---|---|---|
| Seed | November 2022 | $5M | undisclosed | OpenAI Startup Fund |
| Series A | April 2023 | $23M | undisclosed | Sequoia Capital |
| Series B | December 2023 | $80M | $715M | Elad Gil, Kleiner Perkins |
| Series C | July 2024 | $100M | $1.5B | Google Ventures |
| Series D | February 2025 | $300M | $3B | Sequoia Capital |
| Series E | June 2025 | $300M | $5B | Kleiner Perkins, Coatue |
| Series F | December 2025 | $160M | $8B | Andreessen Horowitz |
| Growth round | March 2026 | $200M | $11B | GIC, Sequoia Capital |
The seed round in November 2022 was led by the OpenAI Startup Fund and included angel investors Jeff Dean (then head of Google AI), Elad Gil, and Sarah Guo of Conviction. The April 2023 Series A, led by Sequoia, gave Harvey its first tier-one institutional backing. The December 2023 Series B at a $715 million valuation was notable for a company that had only been public for roughly a year.
The Series D in February 2025 brought in Coatue as a new investor alongside returning backers Sequoia, Kleiner Perkins, OpenAI Startup Fund, Google Ventures, Conviction, and Elad Gil. Particularly significant was the participation of REV, the venture arm of RELX Group, which owns LexisNexis; that investment preceded the formal strategic alliance between Harvey and LexisNexis announced several months later.
By the time of the March 2026 growth round, Harvey had raised over $1 billion in total capital and reached an $11 billion valuation, co-led by GIC and Sequoia, with participation from Andreessen Horowitz, Coatue, Conviction Partners, and Kleiner Perkins.
OpenAI's involvement with Harvey goes well beyond a financial stake. The relationship began at inception: the OpenAI Startup Fund was Harvey's first institutional investor, and OpenAI gave Weinberg and Pereyra pre-release access to GPT-4 when most developers were still working with GPT-3.5-turbo.
In mid-2023, Harvey and OpenAI announced a collaboration to train custom legal models. Harvey worked with ten of the largest law firms to create a fine-tuned caselaw model, running side-by-side comparisons of outputs from the custom model against base GPT-4 responses to the same questions. Attorneys reviewing the outputs rated the custom model meaningfully higher on accuracy and style for legal tasks.
In 2024, Harvey began building on OpenAI's o1 reasoning model, which it used to power legal agent workflows that require multi-step analysis, for instance checking a contract's change-of-control provisions against a deal's transaction structure, then drafting a risk memo summarizing findings. Harvey published a detailed technical post explaining how o1's chain-of-thought capabilities translated to legal reasoning that outperformed standard completion models on tasks requiring sequential logical steps.
Harvey has since expanded its model strategy beyond exclusive dependence on OpenAI, running its platform on multiple underlying models and publishing BigLaw Bench, its open evaluation set, to compare the performance of different frontier models on legal tasks. As of August 2025, GPT-5 scored 89.22% on BigLaw Bench overall, leading competing models from Anthropic and Google. Harvey uses this benchmark data to route tasks to the most capable model for a given task type.
The core product is a conversational AI interface that lawyers use to ask questions, analyze documents, and draft text. Unlike general-purpose chat interfaces, Harvey's assistant is configured with legal-specific behavior: it cites sources, acknowledges uncertainty explicitly, and is trained to flag when a question requires jurisdiction-specific analysis the model may not have. The platform supports more than 100 languages, which matters for the international firms that make up a significant portion of Harvey's client base.
Assistant supports multi-document sessions, allowing a user to load a full deal room worth of contracts and query across them. It integrates with Microsoft Word and Outlook through add-ins, so lawyers can stay inside the tools they already use rather than switching to a separate browser tab.
Vault is Harvey's document repository and bulk analysis product. It stores up to 100,000 documents per vault and is built for the kind of large-scale review work that law firms do in due diligence, litigation discovery, and contract portfolio analysis.
The core capability is structured extraction: a user defines the fields they want to pull from a document set, and Vault queries every document in the repository against those criteria simultaneously, returning a comparison table rather than a stack of individual summaries. Harvey reports 96% accuracy on key-term extraction across the document types it has been trained on.
Vault connects directly to document management systems including iManage, SharePoint, and Google Drive, so firms do not need to re-upload files they already store elsewhere. The product includes granular access controls, which matter for BigLaw because a partner reviewing a document set should not necessarily be able to see a colleague's work product from a different client.
Vault is also accessible from within Harvey's other products: a lawyer using the Assistant can query a Vault directly, and Workflow Agents can run structured extraction protocols across an entire Vault in a single triggered operation.
Harvey's most ambitious product line is its suite of autonomous AI agents. Rather than answering a single question or summarizing a single document, Workflow Agents execute multi-step tasks: they plan an approach, execute sub-tasks, evaluate intermediate results, adjust their strategy, and continue until the goal is met, deciding which tools to use and when to revise their own output.
By early 2026, lawyers and legal teams had built more than 25,000 custom agents on Harvey's platform using the Agent Builder tool. Harvey reports executing more than 700,000 agentic tasks daily. The range of agent types includes:
In one documented case involving M&A due diligence, a team using Harvey's agents reduced review time by more than 80% compared to manual first-pass review by associates.
Harvey developed BigLaw Bench as an open benchmark for evaluating large language models on legal tasks that reflect actual billable work. The benchmark grew out of Harvey's internal evaluation process and was released publicly to give the legal industry a shared standard for comparing AI tools.
The benchmark includes tasks across practice areas including M&A, litigation, regulatory, tax, and employment law. Each task is evaluated against custom rubrics developed with practicing attorneys, measuring completeness, accuracy, citation quality, and appropriateness of the response to the specific legal context. Harvey has expanded BigLaw Bench with several additions: BigLaw Bench: Arena uses pairwise preference comparisons to capture which model outputs experts actually prefer; BigLaw Bench: Global extends coverage to non-U.S. jurisdictions; and BigLaw Bench: Research evaluates legal research quality specifically.
Harvey also published a separate Legal Agent Benchmark (LAB) in 2026 covering more than 1,200 agent tasks across 24 practice areas, evaluated against more than 75,000 expert-written rubric criteria.
Allen & Overy, the London-headquartered global law firm, became Harvey's first major enterprise client in December 2022, deploying the platform across its practice before Harvey had emerged from stealth. That early partnership gave Harvey access to the kind of high-quality legal work product that helped it tune its models, and it gave Allen & Overy a head start on understanding what AI could realistically do in BigLaw.
Following Allen & Overy's 2024 merger with Shearman & Sterling to form A&O Shearman, the partnership deepened. In April 2025, A&O Shearman and Harvey announced a co-development agreement for agentic legal workflows, with A&O Shearman contributing senior attorney expertise to train agents across four initial practice areas: antitrust filing analysis, cybersecurity, fund formation, and loan review. Unusually for the industry, the agreement included a profit-sharing arrangement by which A&O Shearman participates in revenue Harvey generates from selling those co-developed agents to other firms and clients. This arrangement was among the first of its kind in legal AI, signaling a model in which law firms become co-developers rather than pure customers.
In March 2023, PricewaterhouseCoopers announced a strategic alliance with Harvey giving PwC's legal professionals exclusive Big Four access to the platform. The alliance extended to PwC's network of more than 4,000 legal professionals in over 100 countries, covering contract analysis, regulatory compliance, claims management, due diligence, and legal advisory work. PwC did not use Harvey to deliver legal advice to clients but rather to support the internal work product of its legal team.
In October 2023, PwC expanded the arrangement into a three-way collaboration with OpenAI to develop domain-specific foundation models trained on PwC's proprietary data for tax, legal, and HR services. The three-way deal gave PwC the ability to train models on its own client work product within Harvey's infrastructure.
In June 2025, Harvey and LexisNexis announced a strategic alliance to integrate LexisNexis legal content and AI technology directly into Harvey's platform. The partnership was notable partly because RELX Group, LexisNexis's parent company, had already invested in Harvey through its REV venture arm during the February 2025 Series D.
Under the alliance, Harvey customers can access LexisNexis primary law content, including U.S. case law and statutes, validated through Shepard's Citations, without requiring a separate LexisNexis subscription. The integration uses LexisNexis-finetuned models that anchor responses in LexisNexis's legal content database, powered by Shepard's Knowledge Graph and Point of Law Graph technology to trace legal authority.
The partnership also included co-development of motion-specific workflows. The first two were a Motion to Dismiss workflow that generates arguments grounded in LexisNexis case law, and a Motion for Summary Judgment workflow that automates drafting with supporting research pulled from LexisNexis content. Both workflows were positioned as agentic: a lawyer triggers the workflow, provides the facts and procedural context, and Harvey's agent produces a research-grounded draft.
The alliance positioned Harvey as a potential replacement for standalone legal research subscriptions, which rattled some observers in the legal information market who noted that LexisNexis was effectively helping a product that could compete with its own research tools.
Harvey's client roster expanded substantially through 2024 and 2025. Notable deployments include:
By the time of the March 2026 fundraise, Harvey reported more than 1,300 customer organizations across 60 countries, including a majority of the AmLaw 100, 500+ in-house legal teams, and 50 asset management firms.
Due diligence is the most-cited use case for Harvey. In a typical M&A transaction, associates spend hundreds of hours reviewing contracts in a seller's virtual data room to identify provisions that could affect deal terms or create post-closing liability. Harvey's agents can ingest an entire data room, apply a structured diligence protocol, flag contracts with change-of-control clauses, assignment restrictions, or unusual indemnification packages, and produce a draft summary memorandum that partners can review and annotate.
The agent approach is more reliable than one-shot summarization because it breaks the task into steps, evaluates its own intermediate outputs, and can re-query documents when initial answers are ambiguous. Harvey reports that M&A teams using its agents have reduced first-pass diligence time by over 80% in documented cases.
Harvey's Vault and Workflow Agents support contract review at scale. A corporate legal team managing a supplier contract portfolio of thousands of documents can load the full set into Vault, run a structured extraction protocol, and receive a table comparing key terms (pricing, term, auto-renewal, limitation of liability, governing law) across every contract simultaneously. The same workflow can flag non-standard deviations from company playbook positions, which would otherwise require an attorney to read every contract line by line.
For transactional lawyers handling individual contracts, Harvey's Word add-in supports real-time drafting assistance, clause suggestions based on precedent, and risk flagging as a lawyer reviews a counterparty's markup.
Harvey's research capability combines its underlying language models with, for U.S. law tasks, the LexisNexis integration to provide research answers grounded in cited case law and statutes. A lawyer can ask a research question in natural language and receive an answer with Shepard's-validated citations, rather than a list of raw search results to review manually.
BigLaw Bench: Research benchmarks Harvey's research quality against competing AI research tools. The benchmark measures citation accuracy, answer completeness, and the model's ability to identify when controlling authority conflicts with a client's preferred position.
Harvey supports regulatory and compliance tasks including monitoring regulatory changes across jurisdictions, analyzing the impact of new rules on client business models, and drafting compliance memoranda. This is particularly relevant for global law firms and in-house legal teams at multinational corporations, which need to track legal developments across dozens of jurisdictions simultaneously. Harvey's multi-language support means that a compliance team at a European company can query German, French, and Spanish regulatory materials within the same interface.
In litigation, Harvey's agents support discovery review by applying classification protocols across large document productions, flagging documents that hit on relevant search criteria and categorizing them by issue. Vault's discovery table functionality converts sprawling document sets into structured review tables that litigation teams can sort, filter, and annotate.
Harvey also supports brief drafting through its LexisNexis-powered motion workflows, which generate argument drafts grounded in current case law for common motion types.
Harvey has received broadly positive coverage within the legal technology industry, with law firms praising the quality of its outputs relative to general-purpose AI tools and citing its legal-specific training as a key differentiator. TIME named Harvey to its Time100 Most Influential Companies list for 2025.
Several law schools have partnered with Harvey to offer law students access to the platform, with Harvey expanding its law school program to UK institutions in November 2025. Harvey framed this as an investment in training the next generation of lawyers to work effectively alongside AI, while critics noted that the program also serves as an effective marketing channel.
The Slush 2025 technology conference featured Harvey as a case study in domain-specific AI, with Weinberg arguing that the path to reliable professional AI runs through deep specialization rather than generalist capability.
Investor reception has been exceptionally strong by any measure. Eight funding rounds in under four years, each at a substantially higher valuation, reflect conviction among tier-one venture investors that Harvey is building durable competitive advantages in a large addressable market.
The most serious limitation of any AI legal tool is the tendency to generate plausible-sounding but incorrect legal citations, a problem known as hallucination. This is not trivial in legal practice: a brief citing a case that does not exist is a false statement to a court. Federal courts imposed more than $50,000 in sanctions against attorneys in the Mata v. Avianca case in 2023 after OpenAI's ChatGPT generated entirely fabricated case citations that were filed without verification.
Harvey has invested substantially in reducing hallucination rates. The company published internal data showing that Harvey's Assistant model hallucinates approximately 0.2% of claims (roughly 1 in 500) on BigLaw Bench tasks. Gabriel Pereyra has argued publicly that a 0% hallucination requirement is the wrong standard for evaluating AI legal tools, drawing an analogy to the error rate that human associates produce, and contending that any tool that reduces total error rates compared to unassisted work adds value even if it is not perfect.
This argument has not satisfied all critics. A 2024 empirical study published in the Journal of Empirical Legal Studies found hallucination rates exceeding 30% and accuracy below 50% for complex document tasks across a range of legal AI tools, including some that do not use Harvey's architecture. The study also found that Thomson Reuters' Ask Practical Law AI provided accurate responses only 18% of the time on its test set. Harvey was not independently evaluated in that specific study, but the broader finding that hallucination remains a serious and unresolved problem across the legal AI category created difficult questions for the industry.
The LexisNexis integration partly addresses this problem for research tasks by anchoring answers in Shepard's-validated citations rather than model-generated authority. But a large share of Harvey's use cases, including due diligence, contract review, and drafting, do not rely on external citation databases, and the hallucination risk in those tasks is harder to independently verify.
Bar regulators in the United States and the United Kingdom have issued guidance requiring lawyers to review and verify AI-generated output before submitting it to courts or clients. Harvey's terms of service and its training materials for law firms both emphasize that the platform is a drafting and analysis tool, not a substitute for attorney judgment. This means the productivity gains Harvey offers depend on the lawyer's willingness to critically review output rather than accept it, which can reduce time savings if lawyers feel they need to re-verify everything the platform produces.
Harvey's enterprise pricing, which requires a minimum commitment of roughly 20 seats at approximately $1,000 to $1,200 per seat per month, puts it out of reach for most small law firms and solo practitioners. This means Harvey's benefits flow primarily to large firms and well-resourced corporate legal departments, which already have significant technology advantages over smaller practices. Critics have argued that this dynamic risks accelerating the two-tier structure of the legal market rather than democratizing access to high-quality legal analysis.
Some legal professionals have raised concerns about the long-term effects of AI legal tools on associate employment. If agents can perform first-pass due diligence that would previously have been done by a team of junior associates, law firms could accomplish the same volume of work with fewer people or without billing clients for as many associate hours. Harvey and the law firms that use it have generally characterized the technology as augmenting lawyers rather than replacing them, citing the need for attorney judgment, client relationships, and strategic advice that AI cannot provide. That argument is harder to make for purely document-processing tasks.
| Platform | Primary use case | Target customer | Underlying model | Notable feature |
|---|---|---|---|---|
| Harvey | Full-service legal AI: drafting, research, due diligence, agents | BigLaw, large in-house teams | Custom fine-tuned models (GPT-4, o1, others) | Vault, custom agents, LexisNexis integration |
| Robin AI | Contract review and negotiation | In-house legal, financial services | GPT-based | Human-in-the-loop managed review |
| Spellbook | Contract drafting and review | Small firms, in-house counsel | GPT-4 (Word add-in) | Microsoft Word native integration |
| CoCounsel (Thomson Reuters) | Research and document work | BigLaw, in-house | Undisclosed | Westlaw and Practical Law integration |
| Ironclad | Contract lifecycle management | Enterprise in-house teams | Undisclosed | End-to-end CLM workflow |
| Leya | Legal research and drafting | European law firms | Undisclosed | Strong Nordic and EU law coverage |
Harvey occupies the highest end of the market by both capability claims and price. Its closest direct competitor in the BigLaw segment is CoCounsel, which is backed by Thomson Reuters and has the benefit of deep Westlaw integration. Harvey's advantage over CoCounsel is its broader task coverage and its agentic workflow infrastructure; CoCounsel's advantage is the depth and reliability of its Westlaw research integration. Spellbook targets a different segment entirely, focusing on solo and small-firm practitioners who need a tool that works inside Microsoft Word at a price point individuals can pay. Robin AI differentiates by combining AI-powered review with optional human review services, which appeals to risk-averse procurement teams that want a human backstop on high-stakes contracts.
Winston Weinberg serves as CEO. Gabriel Pereyra is President and leads the technical side of the company. Harvey hired Gordon Moodie, a partner from the elite New York firm Wachtell, Lipton, Rosen & Katz, as Chief Product Officer in July 2023, a hire that gave Harvey credibility within the AmLaw community. The company had approximately 400 employees as of late 2025.
Harvey grew from approximately $10 million in annual recurring revenue at the end of 2023 to $65.8 million at the end of 2024, a 558% year-over-year increase. The company crossed $100 million in ARR in August 2025 and reached approximately $190 to $195 million in ARR by the end of 2025, roughly a 3.9x increase in a single year. Customer count grew from 40 organizations in early 2024 to more than 1,300 by early 2026, with users spanning 60 countries.