CloudNC, a London-based manufacturing-software company, has raised $20 million in a Series B extension led by Nimble Ventures, with participation from Calculus Venture Capital, Entrepreneur First and LM Ventures, the venture capital arm of defence contractor Lockheed Martin. The round, announced September 9, brings CloudNC's total disclosed funding to $128 million as the company continues scaling its AI-powered computer numerical control programming software.
CloudNC's flagship product, CAM Assist, uses artificial intelligence to automate portions of the programming process required to operate CNC machines, which manufacture precision components used across aerospace, automotive, defence and consumer hardware industries. Traditionally, configuring how a given part should be machined has required significant manual expertise from skilled machinists, a bottleneck that has constrained manufacturing throughput even as demand for precision-engineered components continues to grow across multiple industrial sectors.
According to the company, more than 1,000 machine shops worldwide now use its technology, including hundreds of facilities across the United States, underscoring the breadth of adoption CloudNC has achieved within a traditionally conservative and fragmented manufacturing software market. The participation of Lockheed Martin's venture arm is particularly notable, signalling strategic interest from the defence industrial base in technologies that could help address persistent skilled-labour shortages within precision manufacturing supply chains critical to national security production.
While the round's headline figure is modest relative to some of the larger AI application financings disclosed this week, industry observers have flagged CloudNC as one of the clearest examples of artificial intelligence meaningfully entering the physical industrial base, rather than remaining confined to software, media and knowledge-work applications that have dominated the current wave of AI investment. Manufacturing represents one of the largest segments of the global economy where AI-driven productivity gains remain comparatively underexploited, given the sector's reliance on specialised physical processes and tacit operator knowledge that has historically resisted straightforward automation.




