“Do more than humanly possible” – legal AI pioneer receives millions in funding


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Leading legal artificial intelligence company ROSS Intelligence has unveiled a new $8.7m (£6.6m) funding round, some of its from Nextlaw Labs, the business accelerator backed by UK-based global firm Dentons.

It was joined by Comcast Ventures Catalyst Fund, Y Combinator Continuity Fund, Real Ventures and Apple’s deep learning lead, Nicolas Pinto. The money adds to the $4.3m raised in ROSS’s seed funding round, of which Nextlaw was also a part.

A little over two years old, ROSS Intelligence – based in Canada and the US – has developed AI technology that can read and answer questions about the law, streamlining legal research processes.

It says it has clients in almost every state in the US and they include Latham & Watkins, Dentons, Shearman & Sterling, Sidley Austin, Bryan Cave and K&L Gates.

The company said it would use the additional financing to expand its current offerings into other practice areas – with employment law coming next – launch new product lines outside of legal research, “accelerate product growth and innovation”, and invest in expanding sales and marketing resources.

CEO Andrew Arruda said: “Our goal is to continue to bring the power of artificial intelligence to the legal industry, allowing lawyers to enhance and scale their abilities and do more than ever before humanly possible.”

Mr Arruda told Legal Futures that the plan was eventually to bring the technology to the UK, but for the time being he was focused on the US.

He added: “This capital infusion allows us to continue our aggressive growth, continue to fill our team with fabulous hires… head into new practice areas and also start scoping out other potential product lines.”

Kai Bond, principal of Comcast Ventures Catalyst Fund, said: “We pride ourselves in identifying the companies who will bring transformative change to their verticals. ROSS Intelligence is challenging the methods of the traditional legal industry with their transformational NLP and machine learning-fueled approach.”




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