Full-Time
Harvey
501-1,000 employees
Custom AI solutions for law firms
No salary listed
Mid, Senior
London, UK
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- 4+ years of experience in a customer support or user operations role, with at least 2 years in a leadership capacity.
- Proven experience managing and scaling support teams in a fast-paced environment.
- Strong ability to balance strategic thinking with hands-on execution in daily support operations.
- Experience supporting enterprise and mid-market customers, with a focus on white-glove service and operational efficiency.
- Deep familiarity with Freshdesk or similar customer support platforms.
- Excellent problem-solving skills and a data-driven approach to decision-making.
- Strong communication and collaboration skills, with experience working cross-functionally across Product, Engineering, and Customer Success teams.
- A passion for customer experience and a drive to continuously improve support processes and outcomes.
- Lead and manage a small team of User Operations Specialists, fostering a culture of customer-first problem-solving and operational excellence.
- Ensure best-in-class customer support, particularly for enterprise customers, by refining processes and optimizing support workflows.
- Drive efficiency and scalability in customer support by developing self-service resources, knowledge bases, and automation strategies.
- Monitor team performance and KPIs, ensuring alignment with company-wide goals for customer satisfaction and operational effectiveness.
- Collaborate with Product, Engineering, and Go-to-Market teams to ensure smooth escalation processes and feedback loops for product improvements.
- Oversee support tooling and infrastructure, particularly leveraging Intercom, to enhance customer interactions and streamline operations.
- Analyze customer support data to identify trends, improve processes, and enhance overall user experience.
- Hire, train, and develop team members, ensuring a high level of expertise and continuous learning within the team.
Harvey.ai builds custom Large Language Models (LLMs) specifically for top law firms to help them tackle complex legal challenges across different areas and jurisdictions. Their AI technology streamlines operations, reduces manual tasks, and enhances decision-making processes. A notable product is their AI chatbot, developed in collaboration with Allen & Overy, which demonstrates significant efficiency improvements in legal work. Unlike many competitors, Harvey.ai focuses on bespoke AI solutions tailored to the unique needs of elite law firms, charging for both initial customization and ongoing subscription services for support and updates. The company's goal is to enhance the efficiency and accuracy of legal practices while ensuring high standards of data security, supported by a dedicated Security Advisory Board and leading security certifications.
Company Size
501-1,000
Company Stage
Series D
Total Funding
$506M
Headquarters
San Francisco, California
Founded
2022
Simplify's Take
What believers are saying
- Harvey raised $300 million in Series D funding, valuing the company at $3 billion.
- Opening a London office positions Harvey to serve European clients and expand market reach.
- LexisNexis' investment in Harvey indicates potential for cross-industry collaboration and growth.
What critics are saying
- LexisNexis' investment could lead to conflicts of interest as they are competitors.
- The trend of using smaller language models could challenge Harvey's focus on large LLMs.
- International expansion, like the London office, may expose Harvey to regulatory challenges.
What makes Harvey unique
- Harvey provides a natural language interface for legal workflows, simplifying complex tasks.
- The company specializes in custom LLMs for elite law firms, addressing complex legal challenges.
- Harvey's BigLaw Bench sets a benchmark for AI accuracy in legal tasks.
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Benefits
Relocation Assistance
Growth & Insights and Company News
6 month growth
↑ 2%1 year growth
↑ 52%2 year growth
↑ 70%Startups that build artificial intelligence (AI) applications on top of large language models (LLMs) are reportedly making rapid gains in both sales and funding. These startups are reaching as much as $200 million in annual recurring revenue in less than two years and, as a group, increased the amount of funding they attracted by 110% to reach $8.2 billion in 2024, the Financial Times (FT) reported Monday (April 14), citing data from Dealroom.co and Flashpoint. This category of AI startups includes companies like Perplexity, Synthesia, ElevenLabs, Harvey and Sierra, according to the report
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. When legal research company LexisNexis created its AI assistant Protégé, it wanted to figure out the best way to leverage its expertise without deploying a large model. Protégé aims to help lawyers, associates and paralegals write and proof legal documents and ensure that anything they cite in complaints and briefs is accurate. However, LexisNexis didn’t want a general legal AI assistant; they wanted to build one that learns a firm’s workflow and is more customizable. LexisNexis saw the opportunity to bring the power of large language models (LLMs) from Anthropic and Mistral and find the best models that answer user questions the best, Jeff Riehl, CTO of LexisNexis Legal and Professional, told VentureBeat.“We use the best model for the specific use case as part of our multi-model approach. We use the model that provides the best result with the fastest response time,” Riehl said. “For some use cases, that will be a small language model like Mistral or we perform distillation to improve performance and reduce cost.”While LLMs still provide value in building AI applications, some organizations turn to using small language models (SLMs) or distilling LLMs to become small versions of the same model. Distillation, where an LLM “teaches” a smaller model, has become a popular method for many organizations. Small models often work best for apps like chatbots or simple code completion, which is what LexisNexis wanted to use for Protégé. This is not the first time LexisNexis built AI applications, even before launching its legal research hub LexisNexis + AI in July 2024.“We have used a lot of AI in the past, which was more around natural language processing, some deep learning and machine learning,” Riehl said
Harvey raised $300 million in a Series D funding round at a $3 billion valuation, led by Sequoia and including investors like Coatue, Kleiner Perkins, and OpenAI Startup Fund. The company experienced 4x ARR growth and expanded to 235 customers in 42 countries, including top US law firms. The funding will enhance the platform and expand enterprise use cases. Harvey also opened a London office as its European HQ. The previous Series C round in July 2024 raised $100 million at a $1.5 billion valuation.
Return investors Sequoia, Kleiner Perkins, GV, Elad Gil, Conviction and OpenAI Startup Fund joined by new investors Coatue and LexisNexis at a $3B valuation.
LexisNexis' parent company, RELX, invested in rival AI firm Harvey through its venture arm, REV, despite Harvey's tools competing with LexisNexis products. Harvey raised $300 million at a $3 billion valuation. RELX claims the investment won't affect LexisNexis' product plans or involve cross-selling. REV aims to support innovative companies, but the strategic benefit to LexisNexis customers remains unclear.