Classical 3D modeling requires hours of manual parameterization. Our system revolutionizes this workflow: we couple state-of-the-art Large Language Models (LLMs) with proprietary, spatially trained Graph Neural Networks (GNNs). The system understands complex, text-based design instructions, generates valid B-Rep (Boundary Representation) geometries, and optimizes them autonomously according to physical parameters.
Select a design instruction (prompt) on the left. The **Geometry Agent** translates the text into numerical CAD commands, while the **Structural Solver** calculates and renders the 3D wireframe model on the right screen.
The technological synergy of linguistic intent recognition and precise geometric boundary value calculations.
Our neural network represents assemblies and geometries as topological graphs. It predicts mechanical dependencies, wall thicknesses, and mounting points, preventing geometrically invalid collisions during generation.
Instead of pure point clouds or triangulated meshes (STL), our agent generates true mathematical surfaces and edges (Boundary Representation). The output is exported directly to standardized STEP or IGES formats for further processing.
The generative system includes an automatic verification agent that performs Finite Element Method (FEM) analysis, simulates material stresses, and iteratively adjusts geometries to comply with required safety factors.