Finnish technology company Quanscient has secured €10 million in a Series A funding round led by 55 North and B&C Group, indicating strong investor confidence in the future of AI-driven hardware engineering. The company is addressing a critical bottleneck in the field; a recent Quanscient study revealed that 89% of engineers routinely simplify their physics models due to runtime limitations. Quanscient’s approach centers on cloud-based multiphysics simulation, generating the vast datasets needed for artificial intelligence to accurately learn real-world physics. “AI will not transform hardware engineering unless simulation itself is rebuilt for it,” says Quanscient co-founder and CEO Juha Riippi, explaining that the platform aims to turn simulation from a constraint into the engine of data-driven design.
€10M Series A Funding Accelerates Quanscient’s Expansion
Led by Danish quantum fund 55 North and Austrian industrial investor B&C Group, the round also saw full participation from existing investors including Maki. vc and QAI Ventures, demonstrating confidence in Quanscient’s approach. The funding will fuel international expansion and further development of a platform designed to unify simulation, quantum algorithms, and artificial intelligence integration. The company’s platform is already gaining traction with Fortune 100 firms across Europe, North America, and Japan, promising simulations up to 100 times faster and a 99% reduction in runtime. Helmut Katzgraber, Chief Science Officer & General Partner at 55 North, believes Quanscient’s technology will be critical for innovators in areas like nuclear fusion, advanced electronics, and quantum technologies. Julia Reilinger, Managing Director at B&C Group, adds that the company is setting a new standard for the development of physical products, reinforcing the potential for long-term industrial innovation across Europe.
Cloud-Scalable Multipysics Simulation Enables AI Integration
This simplification introduces inaccuracies and compromises design quality, and current artificial intelligence models struggle to accurately simulate real-world physics, lacking the necessary data for effective learning. Quanscient is addressing this fundamental limitation by constructing physics simulation as code-driven and cloud-scalable, specifically designed to generate the substantial volume of multiphysics data required for AI training. The company’s platform isn’t simply about faster processing; it aims to fundamentally alter the design process. The platform delivers simulations up to 100 times faster, with runtime reductions of up to 99%, and integrates AI to identify optimal design trade-offs and uncover solutions previously hidden from conventional methods.
AI will not transform hardware engineering unless simulation itself is rebuilt for it. By making multiphysics code-driven and cloud-scalable, we generate the volume of physics data that AI needs, turning simulation from a bottleneck into the engine of data-driven design. This brings to hardware engineering the same shift AI has delivered for software.
Quanscient Platform Delivers Up To 100x Faster R&D
Juha Riippi, co-founder and CEO of Quanscient, envisions a fundamental shift in hardware engineering, driven by a new approach to simulation. The Finnish technology company recently secured €10 million in Series A funding to expand its cloud-based multiphysics simulation platform, a system designed to overcome limitations currently hindering the application of artificial intelligence to physical design. This bottleneck, according to Riippi, stems from the inability of current AI models to accurately simulate real-world physics, a deficiency Quanscient aims to address by generating vast quantities of multiphysics data. “Quanscient’s cloud-native multiphysics platform, combined with forward-looking work on quantum algorithms and AI tools, gives customers a future-proof step-change in throughput without sacrificing accuracy,” he says, emphasizing the potential impact on fields like nuclear fusion and advanced electronics. Riippi adds, “Industrial competitiveness depends on both speed and accuracy,” positioning Quanscient as a key enabler of innovation in physical product development.
Industrial competitiveness depends on both speed and accuracy. The architecture we’ve built for cloud and quantum simulation is also the foundation for an entirely new category of AI and will enable the physics-aware AI models that hardware engineering has been waiting for.
