Limitless Labs Raises $20M Series A to Bring Agentic AI Into Precision Manufacturing

Limitless Labs has raised a $20 million Series A round as the company looks to expand what it describes as the world’s first Agentic Physical AI platform for CAD/CAM-driven manufacturing. The funding was co-led by Dell Technologies Capital and Square Peg, with participation from Grove Ventures, Meron Capital, and Kinetica. The company says the new capital will be used to expand its U.S. operations, accelerate development of its Physical AI Foundation Model, and continue building automation capabilities for CNC programming.

Founded in 2024 and formerly known as LimitlessCNC, the Tel Aviv-based startup is targeting one of manufacturing’s most persistent bottlenecks: the highly specialized process of programming Computer Numerical Control (CNC) Machines used to produce complex aerospace, defense, medical, and industrial components. While much of the AI industry remains focused on digital workflows, Limitless Labs is part of a growing wave of companies attempting to bring AI directly into physical production environments.

Bringing AI Directly Into CAD/CAM Systems

Modern CNC machining still relies heavily on experienced programmers who understand tooling strategies, machine constraints, material properties, and the countless decisions required to transform a CAD model into a manufacturable part.

Limitless Labs has built its platform to operate inside the CAD/CAM software already used by manufacturers, including Siemens NX CAM, Mastercam, and PTC Creo. The company’s CAM Agent is designed to assist engineers by identifying machining features, recommending tooling strategies, sequencing operations, generating toolpaths, and helping create production-ready machine programs. According to the company, customers have reduced CNC programming time by as much as 50% while maintaining human oversight throughout the workflow.

The broader goal is not simply to automate programming tasks, but to capture institutional manufacturing expertise that often exists only in the minds of senior machinists and programmers. The platform is designed to connect engineering data sources, learn from company-specific machining knowledge, and continuously improve recommendations through integration with existing manufacturing systems.

Building a Foundation Model for the Physical World

At the center of the platform is what Limitless Labs calls a Physical AI Foundation Model. Unlike large language models trained primarily on text and code, the company says its model is trained on machining-specific data, including CAD geometry, manufacturing constraints, machine behavior, and the physics of metal cutting.

The concept reflects a broader shift occurring across industrial AI. Rather than relying on generalized AI systems, manufacturers increasingly require models that understand real-world processes, physical limitations, and operational requirements. These specialized systems are being designed to support engineering workflows where precision, reliability, and repeatability are critical.

Rather than functioning as a standalone chatbot, the platform operates as an engineering co-pilot embedded within existing production software. According to the company, the system evaluates machine limits, tooling constraints, feeds, speeds, tolerances, and manufacturing requirements when generating recommendations.

Addressing Manufacturing’s Knowledge Gap

The timing of the raise comes as manufacturers face a growing workforce challenge.

According to data cited by the company, nearly a quarter of the U.S. manufacturing workforce is now 55 years old or older, while 97% of manufacturers identify knowledge retention as a major concern. The industry currently faces more than 400,000 unfilled positions, with that gap projected to grow significantly over the next decade.

For industries such as aerospace, defense, and medical manufacturing, programming errors can be costly, and expertise often requires years of hands-on experience to develop. As veteran machinists retire, manufacturers face increasing pressure to preserve operational knowledge while bringing new engineers up to speed more quickly.

Limitless Labs’ founders argue that AI can serve as a mechanism for capturing and scaling that expertise rather than replacing the engineers who possess it. The company’s platform is designed to learn from historical programming decisions and organizational standards, helping newer engineers benefit from knowledge accumulated over decades of manufacturing experience.

From Pilot Programs to Production Deployments

Since emerging from stealth, the company says it has progressed from early pilots to production deployments with organizations including Blue Origin, Cadillac F1, Sandvik, and Iscar. These deployments span industries where precision manufacturing plays a critical role, including aerospace, motorsports, defense, and industrial machinery.

The company has also emphasized enterprise-grade deployment requirements. Its platform supports cloud, private VPC, and AWS GovCloud environments and is built to meet ITAR compliance requirements, a significant consideration for defense and aerospace customers operating under strict regulatory frameworks.

The Next Phase of Industrial AI

The rise of generative AI has largely been defined by software development, content generation, and enterprise productivity tools. Manufacturing presents a more difficult challenge because AI systems must operate within strict physical constraints where errors can affect production quality, equipment performance, and operational safety.

That is why many industrial AI companies are moving beyond generic language models toward domain-specific systems trained on engineering, manufacturing, and operational data. Across the CAD, simulation, and manufacturing ecosystem, a growing number of startups are building AI agents designed specifically for mechanical engineering workflows, suggesting the sector may be entering a new phase of adoption.

For Limitless Labs, the next objective is advancing its Physical AI Foundation Model toward more autonomous CNC workflows while continuing to expand commercial operations in the United States. The company plans to grow its research team in Tel Aviv and roughly double headcount over the next year.

As AI increasingly moves beyond digital assistants and into physical production environments, the larger question may no longer be whether AI can generate designs or write software. Instead, the focus is shifting toward whether AI can help manage the complexity, expertise, and decision-making required to manufacture the physical products that underpin modern industry.