JuliaHub Raises $65M to Build AI for Industrial Engineering

JuliaHub targets the AI gap in physical engineering, with Fortune 100 clients already deploying the platform across aerospace, automotive, and utilities.

JuliaHub on April 30 closed a $65 million Series B funding round and simultaneously shipped Dyad 3.0, an agentic AI platform built to automate the design and testing of industrial hardware systems, the company disclosed in a blog post. 

Dorilton Capital led the round. General Catalyst, AE Ventures, and Bob Muglia, a technology investor and former CEO of Snowflake, also participated, per the company’s announcement. 

Dyad 3.0 is the third iteration of JuliaHub’s physics-grounded AI engineering environment, following Dyad 1.0 in June 2025 and Dyad 2.0 in December 2025. The platform connects autonomous agents with physics simulations, control analysis, and embedded code generation, allowing engineering teams to build and test digital twins of industrial systems without writing a single line of code. Several Fortune 100 companies are already using the platform across aerospace, government, automotive, HVAC, and utilities, the company said. 

It’s not about helping engineers complete one small task at a time. It’s agentic engineering at scale, where teams can feed a full specification to Dyad and have it design the complete system. Spec in. Design out.
Viral Shah, CEO, JuliaHub

The move comes as industrial engineering remains one of the few sectors where general-purpose AI tools have seen limited adoption. JuliaHub cited internal benchmarking in which general large language model systems, including Codex, Claude Code, and Gemini, completed only the initial setup steps of a chemical process modeling task. Dyad, the company said, nearly automated the full design of model-predictive controllers to optimize a chemical plant’s output, a task that typically takes weeks. 

The platform’s underlying modeling language is built around the laws of physics, enabling agents to reason about fluid dynamics, thermal behavior, and structural forces. JuliaHub said this grounding is what separates Dyad from general AI tools, where an incorrect output in physical engineering is not a software bug but a potential structural or safety failure.

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JuliaHub disclosed one concrete deployment: in partnership with Binnies, a water management firm, and Williams Grand Prix Technologies, the company built a Scientific Machine Learning-powered digital twin that predicts pump faults in water distribution systems with over 90% accuracy using four sensor inputs. 

Dyad represents a step-change for the water industry, enabling a move from reactive operations to predictive, system-level decision making.
Tom Ray, Director of Digital Products & Services, Binnies

Separately, Synopsys confirmed a partnership in which Dyad integrates with Ansys TwinAI to support hybrid digital twins that combine physics-based simulation with data-driven models. Prith Banerjee, Senior Vice President of Innovation at Synopsys, said in the announcement that the integration reduces manual effort across the digital engineering lifecycle. JuliaHub said it will formally demonstrate Dyad 3.0 at a live event on May 19.

The company was founded in 2015 by the creators of Julia, an open-source scientific computing language developed at MIT. Julia is used by more than 1 million developers worldwide, according to JuliaHub. 

Disclaimer: This news is based on publicly available information. NervNow has not independently verified any claims.

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