Luffy AI, an Oxfordshire-based startup using neuroplastic AI for real-time adaptive control, has raised €9.4 million (£8.1 million) in a Series A funding round to drive the startup’s commercialisation pipeline. The round was led by BGF and joined by MIG Capital AG through its MIG Fonds. Existing investors Bow Capital,
Rahul Raj
Luffy AI, an Oxfordshire-based startup using neuroplastic AI for real-time adaptive control, has raised €9.4 million (£8.1 million) in a Series A funding round to drive the startup’s commercialisation pipeline.
The round was led by BGF and joined by MIG Capital AG through its MIG Fonds. Existing investors Bow Capital, Chrysalix, Momenta and UKI2S have also participated in this round.
Dr. Matthew Carr, co-founder and CEO of Luffy AI, said, “AI has been transformative for language and image generation, but has yet to make a substantial impact in industry beyond predictive maintenance and dashboards. Factories, motors and physical systems need AI that is small, fast and adaptive in real time, not cloud-dependent, or with huge data and compute requirements. At Luffy, we’ve already proven what’s possible with AI motor control and will use this new funding to scale up our delivery and rollout.”
Founded in 2019 by Dr. Carr and Dr. Alex Meakins, Luffy AI is building the control layer for physical AI. The company states that adoption of industrial AI is still limited by the data, compute, and connectivity requirements of conventional deep learning. Luffy claims to have developed a neuroplastic AI stack that addresses these challenges and excels in real-time adaptive control.
The UK startup notes that its sparse neural networks are trained in simulation, without the need for large training datasets, and refined in reality, where they can achieve up to 400x greater efficiency than traditional deep learning. The lightweight architecture is highly energy-efficient and self-improving, so it does not require constant retraining from the cloud.
“Our Adaptive Neural Controllers (ANC) learn the physics of a system from first principles and adapt autonomously in real time. No retuning, no cloud, no compromise on speed. They run on the constrained hardware that’s already out there in millions of devices, at timescales that make conventional Deep Learning approaches look Big & Slow,” the company said on its website.
Luffy AI’s ANCs have been benchmarked against the Google DeepMind Real World RL Suite. The company reported 800 x fewer synapses and 400 x less compute required for equivalent or better performance on tasks.
The company highlights that Luffy’s models are ideal for complex edge use cases, such as industrial motors, VFDs, thermal control and robotics. The startup is currently deploying its AI models into industrial motor control and VFD applications, such as industrial pumps, fans and conveyors.
It notes that around 50% of the world’s electrical energy is consumed in an electric motor, the vast majority of which are inefficient. Adaptive AI-based motor control will enable plug-and-play motors that can tune themselves to the load and operating characteristics in deployment. AI control and optimisation will save energy, reduce commissioning time and improve overall motor performance, Luffy mentions.
Kate Ronayne, early-stage investor at BGF, said, “Luffy AI is disrupting an industry norm that has stood for 100 years. Embedding highly specialised AI directly into physical industrial systems reduces reliance on specialist engineers through a self-commissioning, one-size-fits-all approach. The company has taken impressive steps to validate their differentiated technology, and we’re delighted to partner with them as they scale.”
Luffy plans to use this fresh capital to drive the startup’s commercialisation pipeline, moving successful PoCs and pilots into significant partnerships with leading industry brands. In the longer term, the technology can support a wide range of use cases, including positioning control for robotics and drones, thermal process control and physical AI applications.
