Engineering Intelligence for the Physical World

C-Infinity is building foundational artificial intelligence for mechanical design and manufacturing that plans and reasons about complex 3D geometry, motion, spatial constraints, physical feasibility, and production logic.

The old bargain was human planning

Assembly planning, the process of determining the order, orientation, and operations required to put a product's parts together, 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.

Assembly is where complexity multiplies

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.

C-Infinity is building the missing compiler

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 build sequence.

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.

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.