Axiomatic AI has raised $18 million in a seed funding round aimed at developing a new generation of artificial intelligence infrastructure designed specifically for science and engineering applications. The round brings the company’s total funding to $25 million and was led by Engine Ventures, with participation from Kleiner Perkins, Big Sur Ventures, Global Vision Capital, Propagator Ventures, and Liquid 2 Ventures.
The company said the new funding will be used to expand enterprise deployments and deepen the integration of its verification platform into complex engineering and scientific workflows. Axiomatic AI aims to address a growing challenge in advanced technology sectors: ensuring that AI-generated insights and designs are not only useful but also verifiably correct within the laws of physics.
At the core of the company’s platform is a system called Axiomatic Intelligence™, designed to combine advanced AI models with mathematical and physics-based verification methods. Unlike conventional AI systems that often generate plausible outputs without confirming their validity, the platform is built to provide interpretable reasoning that can be formally verified against physical principles and engineering constraints.
The approach is particularly relevant as industries such as semiconductors, photonics, and advanced manufacturing face rising technical complexity. Engineers increasingly rely on simulation and computational design tools, but AI systems still require extensive human oversight to confirm whether outputs are consistent with physical laws and engineering principles.
According to the National Institute of Standards and Technology (NIST), hallucinations and model “cheating” remain major obstacles to deploying large language models safely in critical environments. In high-stakes engineering contexts, inaccurate outputs can introduce design errors, operational risks, or costly delays.
Axiomatic AI’s platform attempts to address these concerns by embedding verification at multiple levels, including fundamental physical laws, domain-specific design principles, and logical reasoning frameworks. This layered verification process allows engineering workflows to be automated while still maintaining traceability and formal validation of results.
The need for such systems is growing as industries confront workforce shortages alongside expanding technological demands. In the semiconductor sector alone, projections indicate the United States could face a shortfall of approximately 160,000 engineering roles by 2032 as domestic manufacturing capacity expands.
“We are defining the standard that science and engineering AI must meet,” said Jake Taylor, CEO of Axiomatic AI. “As demand for the hardware underpinning our economy accelerates, machine learning systems must move beyond assistance into accountable collaboration.”
Axiomatic AI has already launched an early access program that includes several large enterprises across the Fortune 100 and Fortune 500. Participants include semiconductor equipment manufacturers, foundries, fabless design organizations, photonics technology firms, and non-profit research institutions.
Investors say the company’s focus reflects a broader shift in how AI is expected to operate within critical industries. Israel Ruiz, President and General Partner at Engine Ventures, said the transition from predictive AI systems to provable reasoning frameworks will likely shape the next stage of AI adoption.
“Science and engineering are the backbone of modern civilization,” Ruiz said. “The shift from prediction to provable reasoning will define the next era of AI deployment in critical industries.”
Founded in 2024, Axiomatic AI brings together expertise from leading academic and research institutions. The founding team includes Dirk Englund of the Massachusetts Institute of Technology, Frank Koppens of the Institute of Photonic Sciences, Joyce Poon of the University of Toronto, and Marin Soljačić, a physicist and MacArthur Fellow at MIT.
The company is led by Taylor, who previously served as Assistant Director for Quantum Information Science at the White House and as Senior Advisor for Critical and Emerging Technologies at NIST.
Co-founder Englund said the company’s mission is to ensure that scientific reasoning remains central to AI-driven discovery. “Humanity’s greatest achievement—the scientific method—should not be replaced by opaque systems,” he said. “Our goal is to build AI whose reasoning is rooted in mathematics, physics, and interpretability so engineers and scientists remain empowered.”
