CloudNC raises $20 million to automate precise manufacturing pathways, production workshops bet on AI assistant

British manufacturing software company CloudNC announced the closing of a new Series B financing round of $20 million, bringing the total amount raised since its founding to $128 million. The expansion round, led by Nimble Ventures with participation from Calculus Venture Capital, Entrepreneur First and LM Capital, the latter affiliated with Lockheed Martin, comes four years after the company's last major funding round and is intended to direct liquidity toward accelerating adoption of its software and expanding its commercial presence in new markets.

The company was founded in 2015 by Theo Savel, its current CEO, and Chris Emri, chief science officer, to tackle the most complex bottlenecks facing manufacturing processes that use computer numerical control (CNC) machines. These machines are a fundamental pillar for shaping and cutting metals and raw materials with extreme precision to produce parts and structures used in defense, automotive and consumer electronics sectors. Before any machine begins cutting metal, the manufacturing programmer or specialist operator faces a demanding engineering task of determining how to fixture the part, selecting appropriate tools, and setting cutting speeds, feed rates and approach directions.Traditional software systems provide powerful tools, but they leave the programmer with the burden of manually specifying each operational step in detail.

This is where the company's CAM Assist software steps in as an integrated add-on for leading manufacturing suites such as Autodesk Fusion and Mastercam. The system analyzes the part geometrically, proposes suitable tools, entry angles and speed rates, and then generates the machining code the machine needs to start work. The company does not aim to exclude the human expert; instead, it offers the assistant as a digital specialist that sits alongside the programmer to handle initial reasoning and repetitive settings, leaving the human engineer to review, adjust and give final approval to the operating strategy, thereby raising the productivity of skilled staff without abandoning their engineering judgement.

More than a thousand manufacturing workshops worldwide currently rely on the company's software, with 80 % of its customer base located in the United States. The company employs 80 people who are now focusing on expanding the deployment of industrial solutions amid geopolitical shifts that are prompting Western factories to relocate supply chains and localize production, while a sharp shortage of specialized technical labor persists. According to company management, industrial workshops need to accelerate project quoting, program machines faster, and deliver higher output using the equipment and staff they already have, which led the company to develop an additional tool called Quote Agent, expected to launch next month to help workshops evaluate the cost and risk of new projects accurately before accepting or rejecting them.

This shift directly impacts advanced manufacturing and industrial localization projects in the Gulf, Egypt and the Levant, where development plans drive massive investments in precision industries, defense spare parts, automotive components and technical equipment. The biggest obstacle to fully operating local manufacturing workshops and production centers is often not the purchase of modern turning machines, but the scarcity of specialized programmers capable of writing complex toolpaths and programming multi-axis machines. Introducing intelligent assistants into workshop workflows provides a practical solution to bridge this skills gap, enabling factories to boost the efficiency of existing staff and double output without awaiting lengthy training cycles for scarce talent, while automated risk-based quoting gives regional suppliers stronger competitiveness in supply chains and helps avoid losses from manual estimation errors.

The company's experience, which has tested its software for years in its own factory and learned harsh lessons from field operation, confirms that the number of possible ways to machine a single mechanical part far exceeds traditional computational options by a massive margin.The real automation battle is not decided by theoretical models but by testing code and its reliable application on the industrial floor.With the new funding flow, artificial intelligence moves from generating abstract textual and digital designs to controlling the motion of physical cutting tools, turning accumulated operational expertise into software that redefines production flexibility and speed.