Manufacturing Has a Compilation Problem

C-Infinity is building AI that turns complex product designs into feasible assembly plans, closing one of manufacturing's most persistent gaps.

A product can look complete on a screen and still be far from ready to build. The CAD model may show every part in its final position, but it does not necessarily tell a manufacturing team which part should go in first, whether a tool can reach a bolt, or how one design change should alter the production sequence.

That translation from design intent to factory execution is called process planning. It still depends heavily on experienced engineers working through CAD files, spreadsheets, screenshots, and knowledge accumulated over years. The work is essential, but it is also a hidden bottleneck between an idea and a physical product.

C-Infinity is building for that gap. When we led the company's $16 million Series A, our partner Kumar Sreekanti had already spent two years advising CEO Sai Nelaturi. What convinced us was not the promise of adding another AI assistant to the engineering stack. It was the team's decision to build the reasoning layer that stack has long been missing.

Manufacturing software is good at representing designs, managing product data, and tracking the factory floor. But the reasoning between those systems has largely remained human.

That bargain made sense. Assembly planning is not a clerical task that can be automated with a few rules. Real products may contain hundreds or thousands of parts, each with its own shape, movement, tolerances, and dependencies. CAD systems encode geometry, but often not the full intent behind how a product should come together.

The planner has to infer what the designer meant, find an order in which every part can move into place, account for tools and fixtures, and produce instructions people or robots can follow. The answer must be deterministic and explainable. In a factory, a plan that sounds plausible but fails physically is useless.

The difficulty grows faster than the part count suggests. A 20-part assembly has more than two quintillion theoretical orderings before geometry, stability, tooling, and ergonomics eliminate almost all of them. Finding the few workable plans requires evaluating enormous numbers of spatial relationships.

For years, the practical response was to keep experienced engineers in the loop and accept the delay. Exploded views, manual inspection, prototypes, and trial builds are slow, but they catch mistakes older digital tools struggle to see.

The clearest way to understand AutoAssembler, C-Infinity's platform, is as a compiler for the physical world. A software compiler translates human-written code into instructions a computer can execute. AutoAssembler translates a product design into a feasible sequence for building it.

It connects to existing CAD and product lifecycle management systems, then reasons about contact, clearance, interference, motion, and production constraints. From that, it can generate virtual builds, test alternative sequences, flag problems, and produce assembly instructions.

The technical choice underneath the product matters. C-Infinity does not begin with a language model and hope mechanical reasoning emerges. Its approach is geometry-first and grounded in physics. Machine learning helps accelerate search and interpret company-specific rules, while symbolic planning helps ensure the result follows physical constraints.

GPU computing can now process spatial relationships at a scale older CPU-based geometry systems could not. In one published benchmark, C-Infinity's engine analyzed a 3,209-part assembly in 6.8 seconds, compared with 2,660 seconds for CPU baselines. That turns spatial reasoning from a batch process into something engineers can use interactively.

Kumar's conviction centered on that compiler thesis. Language models are powerful, but assembly planning is not fundamentally a language problem. It is a combinatorial problem involving geometry, motion, and constraint. C-Infinity started with the hard reasoning rather than treating it as a feature to add later.

The team had earned the right to take that path. Sai brought deep experience across CAD and digital manufacturing, including work at Carbon and PARC. Chief Scientist Johan de Kleer is a foundational figure in symbolic AI, while CFOO Mats Bergstrom brought operating experience from PARC and prior startups. Together, they understood both the science and how engineering software becomes enterprise infrastructure.

AutoAssembler is already in use across Fortune 100 manufacturers as well as small and mid-sized enterprises, and early deployments have reduced workflows that once took weeks to minutes. The larger signal is that manufacturers are ready for software that participates in engineering decisions rather than simply recording them.

Every complex physical product must cross the distance between a design and a buildable plan. When that crossing is slow, engineers lose time, problems surface later, and factories become less responsive.

C-Infinity matters beyond manufacturing software because loosening this constraint can change how quickly the physical world improves. Faster, more reliable planning can help teams launch products sooner, adapt production with less rework, and spend more engineering time on design rather than reconstruction of intent.

The question C-Infinity is asking is not simply whether AI can generate assembly instructions. It is whether the knowledge required to turn designs into physical products can become computable, reusable, and fast enough to sit inside every engineering workflow.

For us, that is the significance of the missing compiler. CAD vendors own the design, and product lifecycle systems own the data, but the reasoning between design and production has remained largely unclaimed. If C-Infinity succeeds, that reasoning becomes infrastructure, and one of manufacturing's oldest hidden bottlenecks begins to loosen.