Finnish Startup Quanscient Raises €10M to Speed Up Hardware Design With AI
Quanscient, a Finnish company building cloud-based simulation software, has closed a €10 million Series A funding round led by 55 North and B&C Group. The capital will fund international expansion and development of a platform that combines multiphysics simulation with artificial intelligence to accelerate product development cycles.
The company addresses a concrete problem: 89% of engineers simplify their physics models to meet computational deadlines, introducing inaccuracies that compromise designs. Current AI models lack the data needed to learn real-world physics accurately, creating a catch-22 that slows innovation.
How the Platform Works
Quanscient's approach treats simulation as code-driven and cloud-scalable infrastructure designed to generate the massive datasets AI needs for training. The platform delivers simulations up to 100 times faster than conventional methods, with runtime reductions reaching 99%.
By removing computational constraints, engineers can run more complex, accurate models. AI tools then analyze the results to identify optimal design trade-offs and surface solutions conventional methods would miss.
Juha Riippi, Quanscient's CEO, said the shift mirrors what happened in software: "AI will not transform hardware engineering unless simulation itself is rebuilt for it."
Early Traction With Major Companies
Fortune 100 firms across Europe, North America, and Japan are already using the platform. Investors see applications in nuclear fusion, advanced electronics, and quantum technologies-sectors where simulation accuracy directly affects competitive advantage.
Helmut Katzgraber, chief science officer at 55 North, said the technology will be critical for companies developing next-generation physical products. Julia Reilinger, managing director at B&C Group, described Quanscient as setting a new standard for industrial product development.
What This Means for Product Teams
For product development leaders, faster simulation cycles mean shorter design iteration windows and the ability to explore more design variations before committing to prototypes. Integrating AI into the design process reduces reliance on engineering intuition and empirical testing alone.
The tradeoff is learning new tools and workflows. Teams will need to understand how to structure simulation data for AI training and interpret AI-generated design recommendations.
Existing investors Maki.vc and QAI Ventures also participated in the round, signaling continued confidence in the approach.
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