Research
Our research focuses on long-term technical challenges in building, scaling, and operating advanced AI systems.
Key areas of interest include:
Research directions
Exploration of emerging model architectures, system design patterns, distributed training and inference systems, and AI development platforms.
Internal prototypes
Experimental tools, pipelines, infrastructure components, and system prototypes used to validate new ideas in real-world environments.
Theoretical and experimental goals
Investigation of foundational questions in AI engineering, system reliability, efficiency, safety, and human–AI collaboration.
Our work emphasizes practical research that bridges theory and production-grade AI systems.