JuliaHub Raises $65M in Series B Funding Led by Dorilton Capital

The company plans to use the funds to grow its operations and continue developing its products.

Dyad represents a major shift in how physical systems are designed, using AI agents to automate the design and testing of industrial machines. From heat pumps to satellites and semiconductors, engineering teams can reduce design and testing cycles from months to minutes. Leading companies, including several Fortune 100 firms, are already using Dyad and Julia across industries like aerospace, government, automotive, HVAC, and utilities.

Physical engineering is one of the largest industries yet to benefit from AI fully. While tools like Claude Code, Codex, and Gemini have improved software development, industrial engineers are still limited by older tools. McKinsey & Company estimates that $106 trillion will be needed by 2040 to build and upgrade infrastructure. Engineers need faster, AI-driven tools to keep up—and that’s where Dyad comes in.

Dyad gives engineering teams an AI first platform to model, test, and validate industrial systems like “AI coding tools” for the physical world. The latest version, Dyad 3.0, builds on earlier 2025 releases. It combines AI agents with physics simulations, safety checks and control systems, and can generate code for real world machines. This allows teams to design and improve complex systems faster, without needing deep technical expertise.

Dyad uses cloud based AI agents to learn from scientific data and continuously improve system models. It combines real world data, automated testing, and scientific machine learning to keep models accurate and up to date. Engineers can quickly test many designs without writing all the code while still reviewing results to ensure safety and meet requirements.

Dyad’s modeling language is designed to be simple for AI agents to understand and is built on the laws of physics. This allows it to model how systems behave in the real world, such as fluid flow, temperature changes, and forces like gravity, creating reliable models that engineers can trust. For example, in a project with Binnies and Williams Grand Prix Technologies, JuliaHub built a digital twin that uses just four sensors to predict pump failures in water systems with over 90% accuracy.

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Daniel Freeman, who led the Series B round for Dorilton Capital, commented: "Systems modeling is one of the most strategically important layers of the AI-native engineering stack, because it is where physics, control logic, and AI converge. JuliaHub has built something extraordinary with Dyad: a platform that doesn't just model systems, but compiles them, taking engineers from concept to production control code in a single environment. We believe JuliaHub has the potential to become one of the defining companies in Physical AI, and we're proud to back the team as they accelerate Dyad's path to market."

Prith Banerjee, Senior Vice President of Innovation at Synopsys, commenting on the partnership with JuliaHub, says, "Dyad is transforming system-level engineering by combining scientific AI, agentic modeling, and a powerful compilation pipeline into a unified workflow. Integrated with Synopsys simulation software, Ansys TwinAI™ enables high-fidelity hybrid digital twins by combining physics-based simulation with data-driven models. What once required extensive manual effort can now be done far more efficiently, accelerating the entire digital engineering lifecycle and redefining how intelligent, software-defined systems are designed and validated."

"There is a disruptive transition occurring in engineering system design software, and Dyad is on the cutting edge. Previous generations of tools do not provide the promised productivity or integration to unlock the value of AI. With Dyad, you can model the physics, develop control algorithms with auto code generation, and create accurate digital twins and surrogates for rapid development of deep learning inference models, all enabled by AI. Dyad operates where physics meets analytics, and customers and shareholders win!" said David Joyce, former CEO of GE Aviation and Vice Chair of GE.

About JuliaHub

Founded in 2015 by Dr. Viral Shah, Deepak Vinchhi, Dr. Jeff Bezanson, Stefan Karpinski, Prof. Alan Edelman, and Dr. Keno Fischer, JuliaHub is a Scientific AI company built by the creators of the Julia programming language from MIT. It provides advanced tools that combine computing and machine learning to support scientific modeling, digital twins, and simulation, helping engineers and organizations solve complex problems across industries like aerospace and automotive.

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