WilsonAI raises $1.7M for "AI paralegal" model

WilsonAI, a generative AI platform whose model synthesises legal documents, has raised a $1.7M pre-seed round led by Nomad Ventures.

Additional participants include Autopilot Ventures, Entrepreneur First, Transpose Platform, and key strategic angels, including Mei Z, and several exited founders and law firm partners.

WilsonAI is an artificial intelligence platform designed to assist in-house legal teams by automating routine tasks and streamlining legal operations. The platform manages legal requests by triaging inquiries, providing instant answers, and automating workflows, which helps reduce the workload of legal professionals.

It also offers features such as knowledge management, information gathering, and automated reporting, enabling organizations to track key metrics like query resolution times. By handling repetitive queries and tasks, WilsonAI allows legal teams to focus on more complex and high-value activities.

With this funding, WilsonAI will further enhance its AI capabilities, expand its integrations with existing legal tech stacks, and accelerate customer onboarding across industries. The company has already deployed its AI paralegal with 10 technology companies.

 Gus Neate, co-founder and CEO of WilsonAI, commented on the raise: “In-house legal teams face increasing pressure to do more with less, yet they are still bogged down by repetitive tasks. WilsonAI is here to change that. Our AI paralegal augments legal teams at all levels, from General Counsel to junior lawyers, by handling routine work so they can focus on strategic priorities. We're seeing legal teams use the time saved to provide more impactful counsel and take on higher-value projects.ˮ 

Alex Wang, co-founder and CTO of WilsonAI, added: “Weʼre building an AI that doesnʼt just answer questions but actively participates in legal teamsʼ daily workflows. By integrating with in-house legal data and processes, WilsonAIʼs AI paralegal becomes an invaluable team member, delivering efficiency and consistency at scale.ˮ