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Harvey hits $11B valuation to become the platform where legal AI agents actually run work
Law firms and in‑house teams have spent the past two years experimenting with AI, but many of the tools they tried were bolt‑ons: copilots, pilots, and sandboxes that sat on the edge of real work. Harvey’s new funding round is a bet that the core workflows themselves will move onto AI platforms — and that legal teams will let agents run more of the journey from start to finish.
The company, which describes itself as “the legal infrastructure for law firms and in‑house teams,” has raised 200 million dollars in new funding co‑led by returning investors GIC and Sequoia. The round values Harvey at 11 billion dollars and brings its total funding to more than 1 billion dollars, with participation from Andreessen Horowitz, Coatue, Conviction Partners, Elad Gil, Evantic, Kleiner Perkins and others.
Harvey says the capital will go toward scaling the agents customers already run on its platform and expanding the embedded legal engineering teams that help design and maintain those agents. The company’s pitch is not just about adding AI into existing tools, but about becoming the place “where legal workflows run.”
Turning legal work into agent workflows
Harvey’s core idea is that legal work — especially high‑volume, complex tasks — can be expressed as workflows that AI agents execute under human oversight. Instead of treating AI as a drafting assistant or search box, customers are building agents that run multi‑step processes such as M&A due diligence, contract review, and fund formation.
CEO and co‑founder Winston Weinberg frames the shift bluntly: “AI isn’t just assisting lawyers. It’s becoming the system through which legal work gets done.” In this view, agents don’t replace lawyers’ judgment but take over the orchestration of repeatable work so humans can focus on strategy, negotiation, and outcomes.
More than 25,000 custom agents operate on Harvey today, according to the company. These agents execute work across M&A, contract drafting, document review and other core tasks, including “long‑horizon agents” that handle multi‑step workflows over extended periods for complex use cases like fund formation. Some are deployed within Harvey’s Shared Spaces to coordinate work securely across internal teams and external partners.
Embedded legal engineers as part of the product
A key part of Harvey’s story is the role of embedded legal engineers. Rather than leaving customers to build and maintain agents on their own, Harvey deploys teams that work alongside law firms and corporate legal departments to design, refine and monitor the agents that run their work.
Those engineers help translate practice‑specific workflows into something an agent can actually run. That can mean encoding how a firm handles particular types of contracts, or mapping the steps required to perform due diligence in a particular jurisdiction and practice area.
By coupling a platform with ongoing engineering support, Harvey is trying to lower the barrier for firms that know they want automation but lack the internal time or expertise to build a full agent stack themselves. It also gives Harvey a privileged view into how different organizations structure their work, which can inform future product decisions.
From AmLaw 100 to asset managers and enterprises
The funding comes on the heels of rapid customer growth. Harvey says it now partners with the majority of the AmLaw 100, more than 500 in‑house legal teams and 50 asset management firms across 60 countries. Recent customer wins and expansions include NBCUniversal, HSBC, Corrs Chambers Westgarth, DLA Piper International, and McCann Fitzgerald going firm‑wide.
In total, more than 100,000 lawyers across 1,300 organizations run their “most important work” on Harvey, according to the company. That scale underlies Sequoia partner Pat Grady’s description of Harvey as “the platform on which legal work runs,” and his comment that co‑leading three rounds in the same company is rare for Sequoia.
The customer base shows how Harvey is pushing beyond early adopters to a mix of global firms, regional players and enterprise legal departments. Asset managers are also on the list, using agents for workflows that tie together legal, compliance and operations across funds.
Positioning as the operating system for legal and professional services
Harvey increasingly talks about itself not just as a legal tool but as “the operating system for legal and professional services.” Its products streamline workflows in contract analysis, due diligence, compliance, and litigation, with a focus on driving efficiency and faster decision‑making.
That positioning matters because legal is often an entry point into broader professional services. If the platform can prove reliable and secure for high‑stakes legal work, adjacent functions — from compliance to certain consulting workflows — may be more willing to build agents on top of the same infrastructure.
The company backs that story with a roster of investors that includes the OpenAI Startup Fund, Sequoia, Kleiner Perkins, GV, GIC, EQT and others. For investors, Harvey represents a bet that agent platforms built around specific high‑value domains will be durable in a world where general‑purpose models are widely available.
What this round signals for agent platforms
Harvey’s 11 billion dollar valuation and more than 1 billion dollars in total funding underline how seriously investors and customers now take AI agents in law. The narrative has shifted from “will lawyers use AI?” to “which platforms will become the place where important workflows actually run?”
For law firms and enterprises, that raises both opportunity and risk. Moving work onto agents promises significant efficiency gains, but it also concentrates operational dependency on whichever platform they choose. Harvey is betting that a combination of deep domain focus, embedded engineering support and a growing ecosystem of agents will keep it ahead as others chase the same opportunity.
Sofia Klein is a Staff Writer at futureTEKnow, writing about European AI ecosystems, safety research, and the regulatory frameworks shaping how AI gets deployed.
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