Legal AI startup Harvey has purchased Hexus, a two‑year‑old firm that builds AI‑driven product demo, video, and guide tools. Hexus founder Sakshi Pratap says her San Francisco team has already joined Harvey, while engineers in India will transition after a new Bangalore office opens. The deal aligns with Harvey’s aggressive expansion, as the company recently confirmed an $8 billion valuation after raising $160 million, bringing total funding to $760 million. Harvey now serves more than 1,000 clients in 60 countries, including most of the top U.S. law firms, and aims to accelerate its suite for in‑house legal departments. Leia mais →

Harvey, a legal artificial‑intelligence startup founded in 2022, announced the close of a new funding round led by Andreessen Horowitz that lifts its valuation to $8 billion. The round raised $160 million, adding to earlier $300 million raises that valued the company at $5 billion and $3 billion in the preceding months. Investors now include Andreessen Horowitz, Sequoia, EQT, WndrCo, Kleiner Perkins, Sarah Guo’s Conviction, and Elad Gil. Harvey serves roughly 50 of the top AmLaw 100 firms and corporate legal teams, and it recently reported surpassing $100 million in annual recurring revenue. Leia mais →

Harvey, a legal artificial‑intelligence startup founded by Winston Weinberg and Gabe Pereyra, has attracted top venture investors and grown its valuation dramatically. The company now serves hundreds of clients across dozens of countries, offering AI‑driven drafting, research and document analysis tools. Harvey focuses on a multiplayer platform that handles complex permissioning and data‑residency requirements for law firms and corporate legal teams. While its revenue mix is shifting toward corporate customers, the firm remains seat‑based with plans for outcome‑based pricing as its workflows mature. The startup sees a vast, untapped market for AI in legal work. Leia mais →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Leia mais →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Leia mais →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Leia mais →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Leia mais →

AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output. Leia mais →