Luffy AI Raises €9.4 Mn in Series A Round

The new funding will help Luffy AI expand its commercial operations and bring its technology to more customers. The funding round was led by BGF, with participation from MIG Capital AG through its MIG Fonds. Existing investors Bow Capital, Chrysalix, Momenta, and UKI2S also joined the round.

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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.”

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.”

Founded in 2019 by Dr. Carr and Dr. Alex Meakins, Luffy AI is developing AI technology that acts as the control system for physical machines. The company says many industrial AI systems are limited because they need large amounts of data, computing power, and internet connectivity.

To solve this, Luffy AI has created a neuroplastic AI platform that can quickly adapt and make decisions in real time.

The company says its AI uses sparse neural networks that are first trained in simulation instead of relying on large training datasets. The AI is then improved through real-world use.

According to Luffy AI, its technology can be up to 400 times more efficient than traditional deep learning. It also uses less energy and can continue improving on its own without needing constant cloud-based retraining.