Luffy AI, an Oxfordshire startup developing neuroplastic AI for real-time adaptive control, raised £8.1 mn in Series A funding to expand its commercialisation pipeline, according to Beinsure.
BGF led the round. MIG Capital AG joined through its MIG Fonds, while existing investors Bow Capital, Chrysalix, Momenta and UKI2S also participated.
Luffy AI works on AI control for physical systems rather than language, image generation or software-only workflows. The company argues that industrial AI adoption still runs into familiar limits: heavy data requirements, high compute demand and reliance on cloud connectivity.
Dr Matthew Carr, co-founder and CEO of Luffy AI, said AI has changed language and image generation, but industry still uses it mainly for 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.
Dr Matthew Carr, co-founder and CEO of Luffy AI
Luffy has already validated AI-based motor control and will use the new capital to expand delivery and rollout.
Founded in 2019 by Carr and Dr Alex Meakins, Luffy AI is building a control layer for physical AI. Its neuroplastic AI stack targets real-time adaptive control in industrial settings where conventional deep learning often proves too large, slow or resource-heavy.
The company says its sparse neural networks train in simulation, without large training datasets, then refine performance in real-world conditions.
Luffy reports efficiency gains of up to 400 times against traditional deep learning. Its lightweight architecture also uses less energy and improves without repeated retraining through the cloud.
Luffy’s Adaptive Neural Controllers learn the physics of a system from first principles and adjust autonomously during operation. The company says they run on constrained hardware already deployed across millions of devices, at timescales where conventional deep learning looks bulky and slow.
The startup has benchmarked its ANCs against the Google DeepMind Real World RL Suite. Luffy reported 800 times fewer synapses and 400 times less compute for equivalent or better task performance.
Luffy AI sees strong demand in edge use cases where speed, energy use and hardware limits matter. Those include industrial motors, variable frequency drives, thermal control and robotics.
The company is already deploying its AI models into industrial motor control and VFD applications, including pumps, fans and conveyors. According to Beinsure, this positions Luffy in a large industrial efficiency market rather than a narrow automation niche.
Luffy notes that electric motors consume about 50% of the world’s electrical energy, with many systems still running inefficiently. Adaptive AI-based motor control would allow plug-and-play motors to tune themselves to load and operating conditions after deployment.
AI control and optimisation would reduce energy use, shorten commissioning times and improve motor performance without constant specialist intervention.
Kate Ronayne, early-stage investor at BGF, said Luffy AI is challenging an industrial norm that has lasted for 100 years. She said embedding specialised AI directly into physical systems reduces reliance on specialist engineers through a self-commissioning, one-size-fits-all model.
The company has taken impressive steps to validate their differentiated technology, and we’re delighted to partner with them as they scale.
Kate Ronayne, early-stage investor at BGF
Luffy AI will use the Series A capital to move successful proof-of-concept projects and pilots into larger partnerships with industrial brands. Longer term, the technology also targets robotics and drone positioning control, thermal process control and wider physical AI applications.
