Robin AI, a generative AI startup, has scored $10.5m in a funding round headed by investor Plural.
Also taking part in the round were Episode 1 and angel investors such as Tom Blomfield and senior executives across the legal and private equity industries.
Robin AI’s machine learning model is trained on proprietary data from 4.5 million legal documents, enabling users to draft and negotiate contracts 60-80% faster, saving up to 75% on legal fees.
These savings are enabled by Robin’s unique SaaS + services business model, which combines a ‘lawyer-in-the-loop’ with the latest machine learning technology and the integration of the latest models from Anthropic.
Robin AI has grown revenue 20x since its last fundraise and is already cash flow positive, reviewing over 12,000 contracts per year.
This latest round will drive further development of its tech team and has enabled the partial acquisition of competitor LawGeex, whose customers include UBS, PWC, Ebay and Pepsi.
Robin AI is adding a cohort of these customers to its existing base of private equity and financial services clients including law firm Clifford Chance, and impact fund Blue Earth Capital.
Robin AI CEO and founder Richard Robinson said, “There’s doubt that AI is exploding as a category. New technologies that push the boundaries of what is possible always bring questions of both safety and commercial viability. We believe that AI is bringing about an infrastructure-level change to modern software and our rapid traction in the legal market is a testament to its potential.
“We are very fortunate to be Anthropic’s launch partner for the legal sector – the team’s focus on AI safety aligns with our ‘lawyer-in-the-loop’ SaaS product – deliberately designed to manage the risk of even the most advanced models ‘hallucinating’. We’re using the best models in the world in our product, but we still need experienced human professionals to direct the software to achieve results that work for our customers.
“Our product is already being used in big businesses where trust and accuracy are critical. We’re convinced our business model, using machine learning to help humans work faster, is the model that wins in this space”.
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