Matthew Carr
CEO
Luffy AI
The startup develops adaptive AI control systems that optimize the performance of industrial manufacturing equipment by learning operational nuances and adjusting to changes over time. This technology enables businesses to enhance efficiency and reliability in processes that traditional AI methods cannot effectively address.
- Artificial Intelligence
- Industrial Automation
- Software Only
Updated 4 months ago
Funding
Raised to date
£8.1MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Est. $10.3Macross 1 round
B+3£8.1M · Series A
Announced Jul 2026
Disclosed cumulative: $10.3M
RoundDateAmountValuationInvestors
Founders
Product
Problem
Many industrial control systems struggle to adapt to changing conditions, equipment wear, and process variations, leading to inefficiencies, downtime, and suboptimal performance. Traditional AI methods often require large datasets and struggle with the continuous variations inherent in industrial processes.
Solution
Luffy AI provides adaptive AI control systems that optimize the performance of industrial manufacturing equipment and robotics by continuously learning and adapting to real-time conditions. Their technology uses a bio-mimetic AI framework based on neuroplasticity to create optimal closed-loop control systems. This "adaption at the edge" eliminates the need for constant retraining in the cloud, enabling real-time adjustments to variations in equipment, processes, and the environment. The AI controllers are trained on first-principles physics models and digital twins, reducing the need for large historical datasets.
Target Audience
The primary customers are industrial companies in sectors such as aerospace, mining, metals, power electrification, marine, manufacturing, and oil & gas, seeking to optimize their control systems for enhanced efficiency, reduced costs, and minimized environmental impact.
Features
- Bio-mimetic AI framework based on neuroplasticity for continuous learning and adaptation
- Real-time closed-loop control systems that optimize performance by adapting to variations in equipment, processes, and environment
- Training on first-principles physics models and digital twins, reducing the need for large historical datasets
- Compute-efficient runtime enabling deployment in embedded controllers, PLCs, DCSs, and SCADA systems
- Robustness to system dynamics, enabling autonomous operation
- High clock speed for millisecond-level computations
- Flexible operation modes to optimize for multiple runtime objectives, such as performance or energy savings
- Advisory systems and co-pilots that leverage physics models to provide insights into plant and equipment