Literal Labs, the Newcastle-based, logic-based AI algorithm pioneer, has appointed B2B software veteran Jim Darragh as non-executive chairman.
Darragh is one of the most accomplished operators in the European B2B software industry, having served as CEO of five international software businesses over the course of his 30-year career. He has previously led Zeus Technology, Abiquo, Ipanema Technologies, CMO Group, and TotalMobile to exit, creating approximately £1.3 billion in enterprise value, with an average return of 5x for investors.
In his most recent role as CEO of TotalMobile, Darragh grew revenues by more than 500% in seven years, and oversaw seven acquisitions. Since stepping down as CEO in 2023, Darragh has been supporting businesses as a board leader, and his shrewd business mindset and experience will be vital as Literal Labs scales the delivery of its world-leading logic-based AI models.
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Noel Hurley, CEO of Literal Labs, said: “Jim is exactly the kind of person we wanted around the table at this stage of our journey. His track record of scaling international software businesses is exceptional, with more than a billion pounds of value created, and a genuine playbook for building teams that execute at pace. Having Jim’s experience and perspective as we grow is a real asset for Literal Labs.”
Darragh added: “I’m incredibly excited about Literal Labs. The opportunity for these unique AI models is significant: as AI continues to move closer to where data is generated, the demand for efficient, high-performance algorithms is only going to grow. Noel and the team have built something with real potential, and I look forward to working with them as they scale.”
Literal Labs, a Prolific North Tech Start-Up to Watch last year, is an AI algorithm company that uses logic-based techniques to generate custom AI models, which benchmarking studies have proven to be orders of magnitude faster, more energy-efficient, and more explainable than neural networks.
Its models are for companies that want to replace GPU-heavy algorithms that are too large, expensive and energy-intensive, for companies that operate in a strict social or regulatory market in which explainability of AI is essential, or for companies needing more efficient AI models for battery-based products.
