AI CNC quoting is extending automation into a part of the machine shop that still relies heavily on experienced people and manual work.
We spoke with CloudNC CEO and Co-Founder Theo Saville about Quote Agent, the company’s AI-powered system for processing RFQs, estimating manufacturing costs and helping shops decide which work to take on.
Quoting is a logical target for automation. Assessing a new job requires an understanding of how the component will be manufactured, how long it will take and where potential problems may arise. In many shops, that knowledge sits with the same experienced engineers and machinists needed elsewhere in the business.
Quote Agent brings costing and cycle-time estimation together with manufacturing analysis, design for manufacture (DFM) feedback and quality documentation, giving CloudNC a presence much earlier in the manufacturing workflow than CAM programming alone.
Hear from Theo Saville as he explains what Quote Agent brings to CNC quoting and how CloudNC is applying AI to the work behind every RFQ.
From RFQ to AI CNC Quote
For many machine shops, preparing a quote still means reviewing drawings, working out the likely manufacturing route, estimating machining time and costs and identifying anything that may complicate production. It can be a substantial amount of work for an order the shop may never win.
Quote Agent automates much of that initial assessment. An RFQ can be put into the system, which then analyses the job and provides information including routing, costing and cycle-time estimates.
The value of this approach depends on manufacturing context. A useful CNC estimate needs to reflect how the component will actually be produced rather than treating quoting as a straightforward pricing exercise.
CloudNC has built that manufacturing analysis into Quote Agent, allowing a shop to review incoming work with much of the initial preparation already completed.
For businesses dealing with a high volume of enquiries, the time saving can quickly become significant. More RFQs can be assessed without adding the same volume of work to the people responsible for estimating and production planning.
Bringing Manufacturing Knowledge Into the Estimate
CNC quoting relies heavily on practical manufacturing knowledge. Geometry, tooling, setup requirements and machining strategy all affect the eventual cost of a component, sometimes substantially.
An apparently small design feature can require a different tool or additional operation. Deep pockets, internal corners and difficult holes can add machining time or introduce production challenges that need to be accounted for before a price is agreed.
Experienced estimators recognise these issues because they understand the manufacturing process behind the drawing. Quote Agent applies AI to the initial analysis, providing the estimator with information that would otherwise have to be worked through manually.
This is particularly relevant in shops where quoting depends on a small number of experienced employees. Their knowledge remains central to the final decision, while less of their time is spent preparing the information needed to make it.
DFM Moves Earlier in the Process
Saville also discusses the time machinists spend dealing with design for manufacture questions. Customers may submit components that can be produced as designed, but where relatively small changes would make production faster, simpler or less expensive.
Quote Agent identifies DFM issues during the quoting stage and provides manufacturing feedback before the job reaches programming or production.
That earlier intervention has practical value for both sides of the RFQ. The manufacturer gets a clearer view of the work involved, while the customer has an opportunity to address problematic features before they become production issues.
It also gives DFM a more formal role in quoting. Rather than relying on someone spotting an issue later in the process, manufacturability becomes part of the initial assessment of the job.
Quality Documentation Joins the Workflow
Saville highlights quality ballooning as another task where CloudNC is applying automation.
Precision manufacturers routinely need to identify and number dimensions and specifications on engineering drawings so they can be linked to inspection documentation. The work is essential in quality-controlled production, although manually preparing those drawings takes time that could be spent elsewhere.
Quote Agent automates quality ballooning and supports first article inspection documentation, bringing quality preparation into the same workflow as quoting and manufacturing analysis.
The broader picture is important here. CloudNC is addressing several tasks that take place between an RFQ arriving and a component being ready for production. Costing is one part of that process, alongside manufacturability checks, quality preparation and, through CAM Assist, CNC programming.
Making Better Use of Skilled Time
The availability of skilled manufacturing people remains a constraint for many machine shops, and quoting can place an unusual demand on that resource.
The people best equipped to estimate complex CNC work tend to understand machines, tooling, materials and production processes in detail. They are also exactly the people a shop needs to solve difficult manufacturing problems and keep work moving through production.
As enquiry volumes increase, the pressure grows. Every RFQ has to be assessed even though only some will become orders, and winning more work generally means processing considerably more opportunities.
Quote Agent reduces the manual preparation involved in that process, allowing experienced staff to spend more of their time reviewing decisions rather than assembling the information behind them.
That distinction is important for understanding CloudNC’s approach to manufacturing AI. Saville’s argument is centred on making scarce manufacturing expertise more productive rather than removing it from the workflow.
From CAM Assist to AI CNC Quoting
Quote Agent also gives a clearer picture of how CloudNC’s AI portfolio is developing.
CAM Assist applies AI to CNC programming, generating machining strategies and toolpaths that programmers can review and refine. Quote Agent moves CloudNC further upstream, bringing automation into the workflow before a CAM program exists and, in many cases, before the shop has decided whether to accept the work.
The resulting process connects areas that have traditionally been handled separately:
RFQ → manufacturing analysis → costing → DFM → quality preparation → CAM programming → CNC production
This direction has been visible in CloudNC’s development for some time. In our earlier interview with Saville about CAM Assist 2.0, he discussed connecting quoting, fixture design, CAM and scheduling into a more integrated AI workflow. CAM Assist was already able to assess machinability and generate cycle-time estimates that could inform quoting.
Quote Agent develops the commercial and pre-production side of that idea, applying automation at the point where a customer enquiry first enters the shop.
Keeping Manufacturing Judgement in the Loop
Faster quoting only helps a machine shop if the estimate accurately reflects the work that reaches production. Material costs, tooling, setup requirements, machine availability, inspection demands and unexpected complexity can all affect the profitability of a job.
Human review therefore remains a key part of the workflow. Quote Agent carries out the initial analysis and prepares information for the estimator, who can then apply commercial judgement and practical knowledge before a quote is finalised.
There is a clear parallel with CAM Assist. AI can generate much of the groundwork, while the programmer or estimator remains responsible for reviewing the result and making the decisions that depend on experience.
For manufacturers considering AI adoption, this may prove a more practical model than pursuing full autonomy from the outset. Automation is applied to the repetitive and time-consuming parts of the process, while people retain control over decisions with financial or production consequences.
MTN Analysis: AI Moves Further Upstream
Quote Agent is another indication that some of the most immediate applications for manufacturing AI may be found before the machine cycle begins.
Much of the industry’s attention has understandably focused on the factory floor. Predictive maintenance, process optimisation, robotics and autonomous production all promise improvements once manufacturing is underway. Yet a substantial amount of skilled work happens before a machine starts cutting.
A subcontractor may review dozens of RFQs, interpret drawings, estimate processes and prepare documentation to win only a portion of that work. The economics are very different from automating a repetitive production operation because the manufacturer is investing engineering time before knowing whether there will be an order.
That makes quoting a particularly interesting use case for AI. Reducing the time required for the first manufacturing assessment changes how many opportunities a shop can realistically process with its existing team.
The important measure will be accuracy rather than raw speed. An estimate produced quickly is of little value if the assumptions behind the cycle time, setup or manufacturing route prove wrong once the job reaches production. Even relatively small discrepancies can erode margins when repeated across multiple jobs.
This is also why CloudNC’s combination of Quote Agent and CAM Assist deserves attention. The two products address different stages of the same process: one deals with understanding and pricing the work coming into the shop, while the other helps turn accepted work into a CNC program.
As those stages become more closely connected, information generated during quoting can increasingly inform the manufacturing process that follows. The longer-term opportunity is a workflow in which less information has to be recreated as a job moves from enquiry to production.
For machine shops, that is potentially more consequential than automating any single task. The larger efficiency gain comes from reducing the manual hand-offs between quoting, engineering, quality and programming while keeping experienced people responsible for the decisions that determine whether the job is commercially and technically viable.
Further Reading
CloudNC Raises $20M to Scale AI CNC Machining and Launch Quote Agent
Our coverage of CloudNC’s $20 million investment, the expansion of CAM Assist and the launch of Quote Agent.
CAM Assist 2.0: Bringing Clarity, Control, and Confidence to AI-Driven CAM
Our earlier conversation with Theo Saville on CAM Assist 2.0, human oversight and CloudNC’s plans to connect more of the CNC manufacturing workflow.
CloudNC Launches CAM Assist for GibbsCAM With AI-Generated CNC Strategies
A closer look at how CloudNC is bringing AI-generated machining strategies into established CAM workflows.
