Tuesday, 06 October 2026
As manufacturers face growing product complexity and increasingly dynamic production requirements, the gap between engineering design and factory-floor execution remains a major challenge. While CAD, PLM, ERP, and MES systems have transformed individual stages of the manufacturing lifecycle, translating product designs into practical, production-ready processes still requires significant engineering effort and judgment. C-Infinity is addressing this gap with AutoAssembler, an AI-powered platform that combines spatial intelligence, computational geometry, and physics-based reasoning to generate and evaluate manufacturing plans. In an exclusive conversation with AI Reporter America, Sai Nelaturi, CEO and Co-Founder of C-Infinity, discussed how AutoAssembler is helping transform manufacturing process planning while enabling engineers to apply their expertise at greater scale.
There has been tremendous innovation in the software used to design products and the technology used to run factories, but there is still a major gap between those two worlds. CAD and PLM systems have transformed engineering, while ERP, MES and automation have transformed production. Yet translating an engineering design into a practical manufacturing process still requires an enormous amount of manual work and engineering judgment.
If you look at a complex product, the CAD model tells you what that product is supposed to look like, and the engineering bill of materials tells you which components are part of it. But neither tells you everything you need to know to actually build it. Someone still has to determine the assembly sequence, decide how parts should be organized into manufacturing structures, understand what tools and fixtures are required, determine whether components can physically be installed in the proposed order, and create the process information that ultimately reaches production. When the design changes, engineers have to determine what changed and what parts of that manufacturing process need to change with it.
That is the gap we started C-Infinity to address. We saw an opportunity to apply AI not simply to manufacturing data, but to the reasoning involved in manufacturing itself. AutoAssembler is designed to understand product geometry, spatial relationships, and physical constraints and use that understanding to help create production-ready manufacturing plans.
I sometimes describe it as a “manufacturing compiler.” In software, a developer writes source code and a compiler translates that intent into instructions a computer can execute. Manufacturing has never really had an equivalent intelligent layer between the digital definition of a product and the physical process required to produce it. We are building that layer.
The larger motivation is that some of the most experienced people in manufacturing spend a tremendous amount of time on work that is important but repetitive: interpreting designs, evaluating sequences, comparing revisions, and reconstructing manufacturing plans when products change. We believe AI can take on much of that computational work while allowing engineers to focus their expertise on the decisions where human judgment adds the most value.
The process begins with understanding the product as a physical object rather than simply treating the CAD model as a collection of data. A complex assembly may contain hundreds or thousands of components, and each of those components has geometry, orientation, and spatial relationships with the parts around it. Those relationships determine what can be assembled, when it can be assembled and how it can move into position.
AutoAssembler analyzes the CAD assembly and builds an understanding of those relationships. It uses spatial intelligence, AI planning and physics-based reasoning to determine constraints on how components can move and come together. From there, it can evaluate potential assembly sequences and identify sequences that are physically feasible.
That is a much more difficult problem than it may initially appear. If you have hundreds of parts, the number of theoretical assembly sequences becomes enormous very quickly. But most of those sequences are not physically possible. A component may become inaccessible once another component is installed. A part may collide with surrounding geometry along the required insertion path. An assembly may become unstable at an intermediate stage. Those physical constraints allow the system to eliminate infeasible options and focus on plans that can actually work.
From there, AutoAssembler can help transform the engineering structure of the product into the manufacturing structure needed to build it. That includes generating manufacturing bills of materials, bills of process, assembly sequences, and related process information that can ultimately flow into the systems used to manage and execute production.
The important point is that this isn’t simply a document-generation exercise. The value comes from reasoning about the underlying product. We want the system to understand why a sequence works, why another one doesn’t, and what manufacturing implications follow from the geometry. That is what makes it possible to create plans that engineers can evaluate and validate rather than simply generating instructions that look plausible.
The engineer remains part of that process. AutoAssembler can generate and evaluate plans, but manufacturing engineers can review them, modify them and apply their own knowledge of the company's equipment, processes and standards. The goal is to combine computational reasoning with human manufacturing expertise.
Traditional manufacturing software has generally been very good at managing information and executing predefined processes. If you give a system a bill of materials, a routing or a set of work instructions, it can store that information, distribute it and track what happens in production. What those systems generally have not done is reason from first principles about how a new physical product should be assembled.
That distinction becomes even more important with the rise of generative AI. Large language models have shown how powerful AI can be when working with language, but manufacturing cannot depend on an answer simply because it appears plausible. If an AI system says that a component should be installed at a particular point in an assembly sequence, that component actually has to fit. There has to be a feasible path to move it into position. It cannot pass through another solid object along the way. The surrounding assembly needs to remain physically viable.
That’s why we combine AI planning with computational geometry and physics-based reasoning. The system is constrained by the physical characteristics of the product. Geometry, motion, accessibility, stability, and sequence all matter.
We think of that as giving AI a form of mechanical intuition. An experienced manufacturing engineer can look at an assembly and immediately recognize things that someone without that experience may miss: this component needs to go in before that one, this fastener will become inaccessible later, or this assembly is going to require support at a certain point. We want AI to be able to reason through those same kinds of relationships computationally.
That also makes the technology applicable to products it hasn’t seen before. The objective is to give the system the ability to reason about new geometry and new constraints. That’s important for enterprise manufacturers because they are constantly developing new products and variants. AI has to be able to work with what comes next, not just automate what has already been done.
Engineering changes are one of the best examples of why manufacturing needs a more intelligent connection between design and production. Products are constantly evolving. A component changes dimensions, a supplier changes, a bracket moves, a fastener is replaced or a subassembly is redesigned. From the design side, that may be a relatively small change. On the manufacturing side, however, it can potentially affect assembly sequences, tooling, fixtures, and work instructions.
The challenge is determining exactly how much of the manufacturing process needs to change. If you have a 1,000-part assembly and only a small number of components have changed, you do not want engineers to rebuild the entire process plan from scratch. At the same time, you cannot simply assume that everything else remains valid, because one geometric change can have consequences elsewhere in the assembly.
AutoAssembler can compare product revisions and identify components that have been added, removed, moved, or otherwise changed. More importantly, it can relate those changes back to the manufacturing process and help determine which portions of an existing plan remain reusable and which need to be reconsidered. That means a validated manufacturing process becomes something the organization can preserve and build upon rather than recreate every time engineering releases a revision. And if most of the product hasn’t changed, the corresponding manufacturing knowledge can be retained while engineers focus their attention on the areas where the change actually matters.
This becomes particularly powerful in high-mix manufacturing and product families. Many manufacturers don’t build just one static product. They are managing different models, configurations, and options that may share a large percentage of their underlying design. There is enormous value in being able to reuse proven manufacturing knowledge across those variations.
Over time, I think we will see manufacturing process planning become much more dynamic. Instead of process plans being relatively static documents that engineers periodically update, they can become living representations of manufacturing knowledge that evolve alongside the product.
One of the biggest opportunities for AI in manufacturing is to move manufacturing feedback earlier in product development. Traditionally, engineering designs a product and then manufacturing determines how to build it. When manufacturing discovers a problem, the issue goes back to engineering, the design changes and the process repeats. The later you discover those problems, the more time and cost they can introduce.
Some problems are straightforward, such as two parts interfering with each other or a fastener becoming inaccessible. Others only become apparent when you consider the assembly as a sequence of physical actions. Every individual component may fit correctly in the final CAD model, but that doesn’t necessarily mean there is a feasible way to assemble the product in the real world.
A component might have to move through another component to reach its final location. Installing one part too early might block access to another. An intermediate assembly might require additional support or fixturing. These are fundamentally spatial and physical problems, which is why geometry and physics-based reasoning are so important.
AutoAssembler evaluates the assembly in that context. It can reason about interference, accessibility, motion paths, sequencing, and stability and use those constraints when developing a manufacturing plan. That allows engineers to identify potential issues before they become problems on the factory floor.
The larger opportunity is to move that intelligence even further upstream. Rather than waiting until a design is essentially finished to ask whether it can be assembled efficiently, manufacturers can begin incorporating manufacturing reasoning while the product is still being designed. A design engineer could understand the manufacturing consequences of a decision much earlier and evaluate alternatives before significant time has been invested downstream.
That changes Design for Manufacturing and Assembly from something that happens at particular review points into something that can increasingly happen continuously throughout product development.
These are critical issues because manufacturing data can represent some of a company's most valuable intellectual property. CAD models, product designs, manufacturing processes, and engineering knowledge are not information manufacturers can simply send to an uncontrolled external AI service. Enterprise AI has to be deployed with that reality in mind.
Our approach is built around giving manufacturers control over their information. AutoAssembler can operate within customer-controlled infrastructure, and a customer's proprietary data and learning remain within that environment. One manufacturer's intellectual property isn’t there to become training data for another manufacturer.
That becomes increasingly important as the system learns from the manufacturer's own process-planning knowledge and from the decisions its engineers make. Every manufacturer has its own way of building things based on its equipment, suppliers, facilities, standards, and accumulated experience. The AI should be able to reflect that organization's manufacturing knowledge without exposing it outside the organization.
At the same time, human oversight is just as important. We don’t view AutoAssembler as replacing the manufacturing engineer. Manufacturing is too complex, and company-specific knowledge is too important, for that to be the goal. Engineers need to be able to review the plans AI generates and modify them where appropriate.
The more interesting model is a collaboration between AI and the engineer. AI can evaluate an enormous number of geometric relationships and potential sequences much faster than a person could manually. The engineer brings context and judgment about how that company actually manufactures products. As engineers refine AI-generated plans, those decisions can also become part of the organization's reusable manufacturing knowledge.
That is where I think AI can have the greatest impact on engineering work: by allowing their expertise to operate at a much greater scale.
For years, manufacturers have talked about creating a digital thread connecting product development with production. We now have an opportunity to make that thread intelligent. Historically, much of the digital thread has focused on ensuring that information can move between systems. The next step is enabling software to reason about that information and understand the manufacturing consequences of what changes along the way.
Imagine a product-development environment where an engineer makes a design change and the manufacturing implications can be evaluated almost immediately. The system identifies what changed, determines which existing manufacturing processes remain valid, analyzes whether the new geometry introduces assembly constraints, and proposes the portions of the manufacturing plan that need to be updated. A manufacturing engineer then reviews those recommendations, makes any necessary refinements, and approves the updated process. That information can then flow downstream toward production.
Now move that capability earlier. As engineers design a product, they could receive continuous feedback about whether it can be assembled and where potential manufacturing difficulties exist. Manufacturing teams could evaluate alternative assembly sequences before physical prototypes are built. Companies could reuse validated process knowledge across product variants instead of repeatedly recreating it. It becomes evident that as robotics and physical AI advance, richer manufacturing plans could eventually provide machines with more of the context they need to execute those processes.
And so begins the collapse of the traditional distance between product design, manufacturing planning, and factory execution. CAD, PLM, ERP, MES, and automation systems don’t disappear. In fact, they remain essential parts of the manufacturing environment. What changes is the intelligence connecting them.
I believe that is one of the next major frontiers for AI. The first wave of generative AI has largely been about understanding and creating digital information. Manufacturing requires AI to take the next step and reason about the physical world. It needs to understand that parts occupy space, that sequence has consequences and that a design is only useful if there is a practical way to build it.
The gap between an engineer's design and the factory floor has persisted because translating product intent into manufacturing instructions requires a tremendous amount of human reasoning. If AI can help capture and scale that reasoning, process planning can evolve from a largely manual bridge between sophisticated systems into an intelligent layer connecting design directly to production.
That is ultimately what we are trying to accomplish with AutoAssembler. We want to shorten the distance between designing something and knowing how to build it, while preserving the manufacturing expertise that makes that possible.
