Our Approach
How We Build
Data-First Methodology
The quality of any AI model depends on the quality and structure of its training data. We have invested years in understanding how to curate, process, and represent 3D data in ways that maximize learning efficiency.
- Custom 3D data processing pipelines
- Multi-format mesh normalization
- Topology-aware data augmentation
- Scalable dataset architecture
Architecture Innovation
Standard image-generation architectures do not directly translate to 3D. We are exploring neural network architectures that natively operate on three-dimensional space, building on research in graph neural networks, geometric transformers, and 3D-aware diffusion models.
- Geometry-aware attention mechanisms
- Hierarchical mesh generation
- Multi-resolution output control
- Conditional generation from text, images, or sketches
Production Integration
We design our models with the end user in mind. Our goal is to minimize or eliminate the manual cleanup typically required before AI-generated assets can enter production workflows.
- Industry-standard format output (FBX, OBJ, glTF)
- Game-engine-ready asset generation
- LOD-aware output for performance optimization
- PBR material generation
