Literal Labs Raises €5.4m to Launch Energy-Efficient AI Models

Newcastle-based Literal Labs secures funding to scale its logic-based AI — promising faster, greener, and more explainable alternatives to neural networks.

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Newcastle-based AI startup Literal Labs has raised €5.4 million in a pre-seed funding round to expand its engineering team and launch its first commercial product by the end of 2025. The company specialises in logic-based AI algorithms that promise to outperform conventional neural networks in speed, energy efficiency, and transparency.

The funding round was led by Northern Gritstone, with co-lead Mercuri, and participation from Sure Valley Ventures, Cambridge Future Tech SPV, and angel investors. Literal Labs was spun out of Newcastle University in 2023 by Dr. Alex Yakovlev and Dr. Rishad Shafik, with support from company builder Cambridge Future Tech. Noel Hurley, former Arm executive, was appointed CEO the same year.

Literal Labs’ models are designed for organisations that need lightweight, efficient AI — particularly those operating in energy-sensitive environments, regulated industries, or with battery-powered products. Instead of relying on data-intensive neural networks, the company’s models use propositional logic, inspired by the work of mathematician Mikhail Tsetlin.

Early benchmarking shows impressive results: 54 times faster inferencing, 52 times lower energy consumption compared to standard neural networks, and up to 250 times the speed of XGBoost in some machine learning tasks.

The company recently doubled its team size, appointing Leon Fedden — formerly AI Deep Learning platform lead at AstraZeneca — as Chief Technology Officer. With the fresh funding, Literal Labs is now poised to scale its engineering team and commercial efforts.

“We’re at a pivotal moment for AI,” said CEO Noel Hurley. “Our logic-based approach delivers the performance companies need, without the energy cost or black-box complexity of neural networks.”

Literal Labs is targeting customers in edge AI, healthcare, and sectors where explainable, sustainable AI is a critical requirement.

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