CloudNC, a UK-based manufacturing software startup, has raised $20 million in a B extension round, bringing its total funding to $128 million. The company's last major raise was four years ago. Founded in 2015 by CEO Theo Saville and chief science officer Chris Emery, CloudNC offers AI-powered CAM Assist software that automates parts of Computer Numerical Control (CNC) machining. CNC uses computer-controlled machines to cut or shape materials into parts for automotive, defence, and consumer hardware industries. The software addresses the ideation phase before parts can be cut, automating decisions about how parts should be held and what tools should be used—tasks typically performed by machinists or computer-aided manufacturing programmers.

CAM Assist, the first AI plug-in for CAM, is now available for GibbsCAM. CloudNC - a manufacturing technology company - today announces that its CAM Assist solution, which uses AI to generate machining strategies for 3 and 3+2 axis CNC machines, is now available for GibbsCAM users. The new software is available as an AI-enabled add-on for GibbsCAM, the CAM software solution used by machine shops to program CNC machines for everything from everyday production work to complex, geometrically-demanding components. The development brings the benefits of CAM Assist to even more machinists around the world, enabling GibbsCAM users to easily program toolpaths and machine parts more efficiently than ever before, within their familiar CAM workflow. Theo Saville, chief executive and co-founder at CloudNC, said: "GibbsCAM is a powerful fit for machine shops that want a smart, flexible CAM solution that can scale from day-to-day production work to produce complex components. Bringing CAM Assist to GibbsCAM helps those users go even faster, meaning programmers can spend less time on repetitive toolpath creation, stay firmly in control of the process, and focus more of their expertise on getting high-quality parts out of the door." Thorsten Strauss, President of GibbsCAM, said: "At GibbsCAM, our focus has always been to provide manufacturers with powerful, practical solutions that help them produce parts more efficiently. By integrating CAM Assist, we are bringing cutting-edge AI technology directly into the workflows our customers already know and trust. This collaboration with CloudNC enables programmers to automate repetitive programming tasks, accelerate part programming, and focus their expertise where it delivers the greatest value, all while maintaining full control of the machining process." CAM Assist uses advanced computer science techniques and AI to generate intuitive machining strategies in minutes or seconds, depending on complexity, which could take CNC machine programmers hours or even days to manually create. As a result, the amount of time it takes to program a CNC machine to make a component - a bottleneck in many factories - is greatly reduced, compared to the previous manual programming process, as is the time spent to estimate how much a new component will cost to program. That gain enables manufacturers using CAM Assist to raise productivity and shorten lead times, while also estimating for more work, more quickly. GibbsCAM users can also immediately benefit from CAM Assist 2.0. The upgrade combines the speed of AI-powered toolpath generation with step-by-step oversight, allowing programmers to configure machines, materials and tool assemblies, assess parts before running, review AI suggestions as they are made, and use the Strategy Editor to inspect, adjust and approve machining strategies before they are sent back into GibbsCAM. CAM Assist is available for GibbsCAM today via CloudNC at https://www.cloudnc.com/gibbscam, or through GibbsCAM's extensive global network of resellers. About CloudNC CloudNC is transforming global manufacturing with AI that accelerates CAM programming, maximizes factory output, and empowers machinists to deliver more. Its core product, CAM Assist, speeds up CNC machining by tackling the most time-consuming and repetitive parts of the process, from machining strategy to toolpath generation - while keeping humans firmly in control. It enables machinists to create effective programs in minutes, unlocking their full potential and helping shops increase throughput and improve consistency. Today, CAM Assist is trusted by over 1,000 machine shops around the world to enhance their teams' productivity, resolving skills shortages and helping them to deliver efficiently for their customers. Founded in 2015, CloudNC consists of a world-class team combining expertise in computer science and physical manufacturing, based in its headquarters in London, UK and its factory in Chelmsford. The technology company is backed by leading venture capital firms Atomico and Episode 1 Ventures, alongside Autodesk and Lockheed Martin as strategic partners. CloudNC Contact: Norval Scott, Global Head of PR and Content, +44 7506 879 782, [email protected] About GibbsCAM. GibbsCAM, a Sandvik company, is a leading CAM solution trusted by manufacturers worldwide for programming CNC machine tools. Known for its ease of use, flexibility, and powerful machining capabilities, GibbsCAM helps manufacturers tackle everything from everyday production work to highly complex machining applications. The software delivers advanced programming solutions that help shops of all sizes improve productivity, reduce programming time, maximize machine performance, and streamline manufacturing processes. Supported by a global network of resellers and application experts, GibbsCAM provides the technology and expertise manufacturers need to succeed in today's competitive manufacturing environment.

Can AI really set CNC cutting parameters? July 1, 2026 That's the question CloudNC Ltd set out to answer in its latest video introducing Cutting Parameters Explorer, or CPE - a feature inside CAM Assist designed to help machinists and CAM programmers set better feeds and speeds, faster. In the film, Jack, Senior Product Owner at CloudNC, takes CPE from the screen to the shop floor. CloudNC Ltd show how it recommends cutting parameters for real machining scenarios, how machinists can adjust those recommendations, and what happens when CloudNC Ltd put them to the test on the machine. Watch the full video below to see CPE in action, including slow-motion footage of the cuts, chip formation, tool engagement and surface finish. Feeds and speeds are one of the most important decisions in machining - and one of the most common places where time, money and performance get left on the table. Run too conservatively, and cycle times become longer than they need to be. Push too hard without understanding the limits, and you risk poor surface finish, worn or broken tools, unstable cutting conditions, or scrapped parts. CPE is built to help solve that problem. Rather than relying on generic book values, fixed presets or trial-and-error adjustments, CPE recommends cutting parameters based on the specific machining context in front of you. That means it is not simply asking: What is the standard feed and speed for this tool? It is asking a better question: What cutting parameters make sense for this specific operation, on this specific setup? What is Cutting Parameters Explorer? Cutting Parameters Explorer is part of CAM Assist, CloudNC's AI solution for accelerating CNC toolpath generation. CPE focuses specifically on cutting data. Its role is to help users generate safe, reliable and productive feeds and speeds much faster than traditional manual methods. A lot of the information required to generate a recommendation is pulled in automatically from CAM Assist. From there, CPE considers the real cutting scenario, including factors such as: * Part material * Machine limits * Tool and holder geometry * Workholding conditions * Operation type * Depth and width of cut * Chip load * Feed rate and spindle speed * Deflection limits * Surface finish targets The result is a recommendation that is tailored to the actual operation, rather than a one-size-fits-all value. How CPE makes its recommendations. CPE uses a combination of physics models and hybrid AI to calculate cutting parameters. It takes into account the geometry of the tool and holder, the material being machined, and the forces likely to be generated during the cut. Those forces matter because they influence tool deflection, vibration, surface finish, cutting stability and, in extreme cases, the risk of tool breakage. The aim is to maximise productivity while staying within acceptable limits. Just as importantly, CPE does not simply produce a number and ask you to trust it blindly. It helps show what is influencing the recommendation. For example, a cut might be limited by spindle power, torque, tool deflection, workholding security, surface finish requirements or another constraint. By making those limits visible, CPE gives machinists and CAM programmers more useful information when deciding how to run the operation. That is a major difference from traditional trial-and-error processes, where you often only discover the limit after you have already reached it. CPE is not about replacing machinist judgement. One of the most important ideas behind CPE is control. The goal is not to force a single recommendation onto every user. Instead, CPE gives machinists a faster, more scientific starting point - and then lets them tune the recommendation depending on what matters most. For example, a user might want to bias the cut towards tool life, stability or surface finish. In another situation, they might want to prioritise productivity and material removal rate. CPE allows users to adjust constraints and regenerate recommendations on the spot. That means the machinist's knowledge of the machine, tool, fixture and part is still central to the process. This is AI making the trade-offs clearer, faster and easier to act on, rather than replacing machining expertise. Why this matters for machinists and CAM programmers. For experienced users, CPE can remove a lot of the time and guesswork involved in setting feeds and speeds for each operation. For less experienced users, it provides a safer and more informed starting point, especially when working with unfamiliar tooling, materials or cutting conditions. For everyone, it makes the constraints visible. That means users are not just accepting a recommendation. They can understand why it makes sense, what is limiting it, and what they could change if they want to go further. The result can be faster cycle times, more consistent programming, better tool life, improved right-first-time performance and fewer compromises from generic parameter presets.