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Business Insider
13-05-2025
- Business
- Business Insider
What Harvey is doing to win the legal AI race it inadvertently started
Legal tech was long a space that investors ignored. Then came Harvey. In just three years, the company, which builds software for analyzing and drafting documents using legally tuned large language models, has drawn blue-chip law firms, Silicon Valley investors, and a stampede of rivals hoping to catch its momentum. Harvey has raised over half a billion dollars in capital, sending its valuation soaring to $3 billion. On a recent Monday afternoon at Harvey's Manhattan office, I met cofounder and chief executive Winston Weinberg in a polished conference room named for Atticus Finch, a beloved character from the novel "To Kill a Mockingbird." I mentioned that since I started covering legal tech a month ago, my inbox has been flooded with pitches from legal-tech startups eager to explain how they're not Harvey. Weinberg let out a soft chuckle. "I'll take that as a compliment, I guess." In recent years, more competition has been encroaching on Harvey's territory, and fast. Hebbia, a knowledge-search platform, has made a more focused push into the legal sector, and Legora, which offers an AI-powered workspace where lawyers can draft, edit, and collaborate, is gaining traction with Harvey's core clientele of Big Law firms. While legal tech was once the domain of ex-lawyers building tools for their peers, now it's attracting classically trained software engineers, eager to compete in a space without a staid market leader. Harvey may have cracked the market open, but now it faces the classic innovator's dilemma: the very proof of concept it offered to the legal world is fueling a growing list of competitors. The legal-tech land grab is on. The question is whether Harvey can maintain its first-mover advantage, or if it's simply cleared the path for the next breakout. Want in? Get in line. Part of what fueled Harvey's ascent is its go-to-market strategy. Early on, the company bet that winning over the country's most elite law firms would create a ripple effect across the industry. It gated access to the product using a waitlist, allowing it to tightly tailor the tool to pilot users. So far, its Big Law wager appears to be paying off. Weinberg said lawyers at eight of the 10 highest-grossing firms in the nation are now using Harvey. The company tells Business Insider it crossed $70 million in annual recurring revenue last quarter, putting it on pace to smash its goal of $100 million ARR for the year. "Once a subset of the market standardizes on a solution, it's kind of the solution," said Ilya Fushman, a venture capitalist who led Kleiner Perkins' investment in Harvey in 2023. Leaning forward in a high-back, caramel-colored leather chair, Weinberg seemed unfazed by the growing competition. Harvey's edge, he argued, lies in two places its rivals can't easily replicate: top-tier talent and a product strategy built on deep collaboration with its customers. The 260-person startup has lured dozens of trained lawyers off the gilded path to Big Law partnership, offering stock options and a shot at shaping the future of legal practice. To keep its edge, Harvey just made a key hire. Stripe veteran John Haddock has joined as chief business officer after a decade scaling one of Silicon Valley's most closely watched startups. Haddock told BI he spoke to dozens of Harvey customers before accepting an offer. His decision boiled down to their love of the product. He called it Weinberg's "No. 1 maniacal focus." "The best thing we can do is stay focused on: are we building stuff that lawyers really need and need every day?" he said. "Everything else takes care of itself." Harvey goes multi-model Harvey has been fighting the competition with one hand tied behind its back. Since its founding, the company has partnered closely with OpenAI to build custom models for lawyers. Its entire product ran on OpenAI's models. It's a limitation that Harvey's rivals have been quick to point out. They argue their products are superior because they can cherry-pick from the best of Anthropic, Meta, or Google, depending on the task. Now, Harvey wants to neutralize that criticism. The company tells BI it's going multi-model, starting with Anthropic's Claude and Google's Gemini. Weinberg said Harvey didn't avoid other models out of loyalty to OpenAI, but necessity. Until recently, most major law firms would only approve AI tools that ran through Microsoft Azure, which meant models like Claude and Gemini couldn't clear security reviews. Those constraints are falling away as vendors like Anthropic build the features enterprises demand and gain clout. The move may also suggest Harvey is adapting to a clientele that's perhaps more opinionated about which models power their tools, especially as rivals pitch flexibility as a selling point. Harvey's secret sauce In a market where model performance offers marginal gains, Harvey is betting that its true edge lies in how deeply the product molds to the client. "What I think is closer to a traditional moat," Weinberg said, "is we are very focused on customization, massively." The company partners directly with firms to build bespoke legal workflow software. With A&O Shearman, for example, Harvey helped develop a merger control tool that taps the firm's global antitrust bench. For another client focused on private equity, the company built out deal-specific workflows. Lawyers across those firms are using the tools, and the firms are selling that customized software to clients and other law firms, sharing a cut of the revenue with Harvey. If customization is the moat, the obvious question becomes: how does it scale? One investor in a Harvey competitor asked at what point the company becomes overrun with service requests and support tickets. The company's bet is that it can turn workflows from custom projects into reusable building blocks — a sort of Lego kit it can adapt for each new client. It's a bold strategy, but in a crowded field, Weinberg believes that winning won't come from better answers. It'll come from building a system that grows with the people asking the questions. "At the end of the day," he said, "what you want to do is build a solution that tracks the evolution of law over time."
Business Insider
02-05-2025
- Business
- Business Insider
From Paul Weiss to DLA Piper, 5 lawyers share how they're using AI at work
Artificial intelligence has a reputation problem in legal circles. Outside the profession, the popular assumption is that attorneys —whose livelihoods still hinge on hours logged—have little incentive to automate themselves out of billable work. Inside the profession, the story is more nuanced. Precision isn't optional in a court filing or a merger agreement, so before any Big Law litigator or solo practitioner lets an algorithm near client work, the tech must meet an uncompromising standard of accuracy and accountability. That bar is finally being cleared, and curiosity is turning into adoption. From global firms juggling multimillion-dollar matters to personal-injury practices compiling medical records, many lawyers are cautiously testing AI for specific tasks, such as summarizing documents, surfacing precedents, and spotting risky clauses. Whether they sit in a glass tower or a strip-mall office, the aim is the same: trim some time from routine steps and, where possible, pass modest savings and clarity onto clients. Business Insider asked five attorneys from Big Law, boutique, and solo practices to share the AI tools they love. Here's how they're using AI — and the guardrails they've built to keep the judgment human. Katherine B. Forrest, partner at Paul Weiss As chair of Paul Weiss' digital technology group, Katherine B. Forrest counsels clients on the thorniest questions surrounding digital assets and artificial intelligence. She also turns to some of the same platforms she advises on for legal work, letting her pressure-test the tools for clients while steadily tightening the gears of her own practice. She's a Harvey power user. Harvey functions as a virtual junior associate: a platform steeped in case law, statutes, and a firm's own documents. Lawyers can dump thousands of contracts, filings, or emails into its secure "vault," then ask the chat interface to summarize, compare clauses, or draft new language on the fly. Every answer comes hyperlinked to the exact source text, so attorneys can audit the reasoning before it ever reaches a client or court. Forrest said she uses Harvey to speed up research and review, not as a substitute for legal judgment. "How do you determine that apart from speed, you've got accuracy and analytical excellence?" she asked. "You've got to have a human who's trained to evaluate that for now." She also uses Hebbia to sift through filings and other documents and answer complex legal questions about their contents. The company sells its software primarily to asset managers and investment banks but is making inroads with law firms like Orrick and Fenwick. Forrest said one tool truly stunned her: ChatGPT's Deep Research. She asked it to assess antitrust risks in a proposed merger and draft a pitch that maximizes the prospect of clearance. She asked three attorneys to vet the output, and they confirmed it was "100% accurate." Drew Morris, partner at First Circle Law When Drew Morris saw the potential of artificial intelligence to provide better legal service at a reasonable rate, he left his general counsel post to start his own supercharged law firm, First Circle Law. He works with early to midsize startups on funding, business contracts, and corporate governance matters. Morris explained that as a solo practitioner, he turns to artificial intelligence tools for all the tasks he would typically delegate. "I use it for everything I would otherwise rely on a junior associate to do," he said. He lives on two platforms. The first is GC AI, which is designed for in-house legal teams. He uses the platform to conduct legal research, create template board consents (a legal mechanism that allows a board of directors to approve a specific action), and redline contracts with proposed changes and revisions. He opens a plug-in from the legal startup Spellbook in Microsoft Word for deeper contract review and editing. Spellbook lets lawyers provide a prompt, such as "modify this agreement to be suitable in California," then, it marks up the contract with suggested changes for the lawyers to accept, reject, or modify. John Hamill, partner at DLA Piper When John Hamill was just starting as an attorney, his firm's partners would hand back paper drafts with their suggestions scribbled in the margins. Then Microsoft changed the game with track changes in Word, Hamill said. Now, the commercial litigator said the legal profession is again changed with generative artificial intelligence. At DLA Piper, where he helps train young attorneys as part of leading the firm's affirmative litigation practice, Hamill encourages his team to use "the latest and greatest software" to sharpen their writing skills and think outside the box. "Some of the tools that are now available on the market can give pretty insightful suggestions to the developing writers on what to do and why," Hamill said. He likes Microsoft Copilot, BriefCatch, and Harvey, among others — though he's reticent to pick a favorite, saying that the best product today could be dramatically different two months from now. Hamill also recognized the potential for copilots to respond with erroneous information. He tells his team always to check the writing assistant's work. "We think of it as a smart, creative intern who works really fast," he said, "but just like a new lawyer, consultant, analyst — it's going to make some mistakes." Sarah Tuthill-Kveton, partner at Chock Barhoum LLP Insurance litigator Sarah Tuthill-Kveton remembers the first time her husband, Scott Kveton, showed her ChatGPT. He used it to whip up a love note. Months later, Kveton outdid himself: He started a company to make his wife's job in personal injury law easier. CaseMark's platform allows lawyers to upload a plaintiff's medical records and receive an auto-generated medical chronology — a detailed, organized timeline of a patient's medical history. Lawyers use these to clarify the sequence of events and build their arguments. Tuthill-Kveton, who defends self-insured companies, insurance carriers, and independent contractors in the gig economy, said reviewing medical records is tedious and slow. She estimates that using CaseMark to produce medical chronologies saves her firm up to a hundred hours each case. The time she gets back, she said, she puts toward taking on more clients. Justin Parsons, partner at Erickson Immigration Group Immigration lawyer Justin Parsons helps tech companies hire top talent from abroad. These days, he's leaning on an artificial intelligence tool called Parley to write first drafts for visa applications. Parley's platform allows an attorney to upload documents such as a client's résumé, college transcript, and job offer letter. It then assembles this evidence to draft an attorney's support letter confirming the applicant's eligibility. Parsons said he's using Parley for various matters, including national interest waivers, EB-1 extraordinary ability petitions, and O-1 visas, a temporary work visa designated for individuals with a record of extraordinary ability in their field. In immigration law, many attorneys charge a flat fee rather than an hourly rate, especially for standard services like visa applications. Parsons said it's for this reason that immigration attorneys are quicker to adopt new technology. "The fees are flat, so it's in our best interest to make things go faster," he said.
Forbes
11-04-2025
- Business
- Forbes
Fintech's Latest Trend: AI Agents For Investment Research
George Sivulka, founder and CEO of Hebbia, a startup that uses AI to improve research for investment bankers and investors. For all the fast adoption of artificial intelligence tools in so many facets of business, AI's entrance into fintech applications has been slow so far. That's largely due to regulatory and compliance hurdles related to handling people's money, industry experts say. To date, most AI features in fintech have focused on speeding up functions like customer service, accounting and other back-office operations, with companies ranging from Klarna and Chime to Stripe and Ramp announcing new AI advancements. Now a new trend in fintech is emerging: using AI for deep investment research. Many companies wading into this space are using AI agents—code that can understand contextual information, make logical decisions and take actions. Agents move beyond research and basic text creation to perform tasks, such as making an investment recommendation or creating a draft PowerPoint presentation. Just over the past month, trading app Robinhood and Arta Finance, a startup that aims to be a digital 'family office' by giving wealthy consumers access to alternative investments, have announced new consumer-facing AI features. An even larger set of emerging companies, nearly all of which seem to be based in New York, are using AI to speed up research for investment bankers and investors. They include outfits like AlphaSense, Hebbia, RavenPack and Rogo. It's hard to tell which of these businesses will live up to their promises and hype. Most are early-stage startups, and the venture capital frenzy for AI companies is still in full swing, making it even more difficult to predict which will build durable businesses. But they're all attacking sizable markets and labor-intensive tasks where improvements are long overdue. Have a story tip? Contact Jeff Kauflin at jkauflin@ or on Signal at jeff.273. Hebbia is a five-year-old New York company that uses AI to try to help financial institutions, law firms and other large companies speed up their research. It raised $130 million in funding last year at a $700 million valuation and is backed by investors including Index Ventures, Peter Thiel and Andreessen Horowitz. Within financial services, Hebbia focuses on analyzing private market data, says 27-year-old founder and CEO George Sivulka. When companies are trying to raise new financing or considering being acquired, they'll typically share confidential information in a secure, virtual data room made accessible to prospective investors. They'll upload information like their audited financials, stock ownership, patents and disclosures on ongoing litigation. Hebbia's software connects to data rooms and tries to answer questions about a company's customer concentration (how reliant it is on a small set of customers), the strength of its revenue growth and the qualifications of its management team. It also aims to identify risks and red flags like how exposed the business might be to tariffs, or whether a topic was glossed over or omitted in an investment pitch even though it's typically covered. Hebbia's software then creates a draft memo or PowerPoint. The company claims private equity firms can save 20 to 30 hours per deal using its products. Like most startups in this space, Hebbia uses a collection of different AI models. It has its own models for retrieving and interpreting investment information from data rooms. It uses outside models from OpenAI, Anthropic and other companies (depending on the customer's preference) for features like generating the text of reports. Sivulka says Hebbia has hundreds of customers, including private equity firm Charlesbank and private credit firm Oak Hill Advisors, and that it charges from 'tens of thousands' to 'millions' for its services. AlphaSense, a 14-year-old financial data company with 6,000 customers, including JPMorgan and Bank of America, also uses AI agents. Like Hebbia, it aims to help analysts with preparing pitches, conducting due diligence research and analyzing markets. For example, for an analyst preparing for a meeting with a large company's CEO or CFO, AlphaSense can spit out research suggesting what the executive's top priorities are, a spokesperson says. It can create a report using experts' testimony to indicate the types of questions institutional investors are asking about Klarna ahead of its IPO roadshow. Rogo is a 40-person, three-year-old New York startup with 40 customers and more than 5,000 active end users, says 26-year-old CEO Gabriel Stengel. According to PitchBook, it was valued at $350 million in a March fundraise, and it's backed by investors including Thrive and Khosla Ventures. Rogo aims to help with a wide variety of labor-intensive analyst duties. It automates the summaries they need to write when a company announces quarterly earnings. Or let's say an investment banker wants to pitch an idea for a large company like ServiceNow to acquire an AI startup in order to improve its own AI capabilities. Rogo can help create a presentation to summarize different AI companies that could be acquisition targets. 'How do you want to compare all these different providers, the startups, the hyperscalers that have done this?' Stengel explains. Another example: Rogo can analyze dating app Bumble's revenue by country by looking at the businesses it owns in different geographies and interpreting the CEO's historical comments. The startup uses its own models to retrieve and interpret data–it tries to train its models to think like an investor. It uses outside models from Google, Meta, Anthropic and OpenAI for other features. For example, if a given task calls for a statistical analysis like a regression, it can use OpenAI's mathematical models. RavenPack is a 22-year-old New York company whose primary business is using data analysis of news and regulatory filings to identify market-moving events for financial institutions like banks and quantitative trading firms. Its customers include JPMorgan, Morgan Stanley, Deutsche Bank and investment bank Nomura, says CEO Armando Gonzalez. Today, it announced a set of new AI features aimed at a wider audience. Through its website analysts can use its AI to create stock watchlists that will send daily, automated research reports. It uses its own models to interpret and retrieve data for initial search queries, and it uses Anthropic to let users query specific documents, like regulatory filings. The site offers a free service with more limited data sources and a limited number of search queries. A premium subscription comes with access to more data sources and starts at $50 a month. The number of new companies popping up in this space is hard to keep track of. BlueFlame AI's website says it helps alternative asset managers like hedge funds make better use of AI large language models. And there's a burgeoning crop of tiny startups–most with fewer than 20 people–that's quickly forming, including businesses like AgentSmyth, BrightWave, Finster AI, ModelML and ProSights. Late last month, trading app Robinhood announced Cortex, a new AI tool for investing that it will launch later this year to customers who pay for its premium Gold service. Like it will provide quick reports on what's going on with a stock. It will also help devise ideas for trades. The product 'makes the options trading experience more intuitive by helping you translate your beliefs about a stock into a specific options trade and strategy,' according to a company blog post. Pulling in data on stock prices, analyst reports and other sources, it will 'screen the market for trades to consider' based on what you've told the app. Last week, Arta Finance announced Arta AI, which makes personalized investment suggestions. It also answers questions like how your portfolio did last month and how a given stock is performing. A subscription will start at $20 a month, and customers with more than $100,000 managed by Arta can get the service for free.
