Synthetic Data Generation
Engineering-grade data generated directly from structured systems, not visual approximations.
Scales on

and open industrial ecosystems
Programmatically generate standardized, industrial-grade SimReady assets and environments, so your team can focus on simulation and training—not manual modeling or cleanup.
Instead of a single “perfect” setup, systematically vary layouts, parameters, and operating conditions to expand training scenarios.
Move beyond isolated tasks to system-level workflows, long-horizon behavior, and multi-objective training.
OUR DATA PIPELINE


FEATURE
MetAI operates as the simulation data layer between physical systems and AI models—transforming system structure, behavior, and control logic into scalable training, validation, and decision data.
Engineering-grade data generated directly from structured systems, not visual approximations.
Systematically generate rare, failure, and stress scenarios at scale, especially those that are hard, risky, or impractical to collect in real-world environments.
Data grounded in actual layouts, constraints, and operational logic.
Training data that reflects real system behavior, control logic, and decision pathways.
INDUSTRY
1MetGen For
Warehouse
Simulate warehouse systems from planning to operations to optimize productivity and enable automation and robotics at scale.
2MetGen For
Semiconductor
Simulate one of the most complex industrial environments to validate, plan, and train Physical AI at scale.
3MetGen For
Data Center
Model operational constraints and edge conditions to train AI systems for reliability, safety, and long-horizon coordination.