Metalnx uses AI-driven material design to create light metal alloys, semi‑finished parts, and components with near‑zero carbon emissions. Its platform predicts cost, performance, and sustainability, enabling manufacturers in automotive, aerospace, and industrial equipment to rapidly iterate alloy compositions and scale low‑emission production. By integrating real‑world data, Metalnx continuously optimizes manufacturing processes to meet high‑performance requirements while reducing CO₂ footprints.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional metal manufacturing processes generate high CO₂ emissions, making it difficult for industries to meet sustainability targets while maintaining performance and cost efficiency.
Solution
MetalNx applies advanced artificial‑intelligence algorithms to the design and development of metal products, enabling the creation of raw materials, semi‑finished parts, and components with a near‑zero carbon footprint. The AI models are trained on real‑world data to predict cost, performance, and sustainability outcomes, allowing rapid iteration and optimization of alloy compositions and processing routes. By industrialising these AI‑driven designs, MetalNx delivers bespoke, low‑emission metal solutions that meet the specific requirements of diverse advanced manufacturing applications. The platform integrates the AI workflow with manufacturing expertise to scale production while reducing the overall CO₂ emissions of metal components.
Target Audience
Primary customers are manufacturers and engineering firms in sectors such as automotive, aerospace, and industrial equipment that require high‑performance metal parts while pursuing aggressive carbon‑reduction goals.
Features
- AI‑based material design engine that forecasts cost, mechanical performance, and carbon footprint for light metal alloys
- Data‑driven optimization of manufacturing processes to achieve near‑zero emission production pathways
- Customizable solutions for raw materials, semi‑finished products, and finished metal components across multiple industries
- End‑to‑end workflow that bridges AI design with industrial scale‑up and supply chain integration
- Continuous learning loop that incorporates real‑world production data to improve sustainability predictions over time