Interactive worlds for people and AI to build, learn and solve problems together
Stage
Not given
Not given
Founded
Not given
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Based in
US
Not given
Pricing model
Paid
Free
Pricing
Usage-based pay-as-you-go pricing with hourly compute charges; committed contracts available for volume discounts. Hosted option with Anyscale-managed infrastructure or Bring Your Own Cloud deployment available.
Not given
Key features
Multimodal data curation at scale
Distributed model training with elastic scaling
Batch embedding generation at scale
Multi-cloud deployment and orchestration
Cluster-backed development environments
Workload-specific observability and debugging
Production-grade managed Ray clusters
Access control and governance
GPU budget controls and cost attribution
Post-training with SkyRL and veRL support
Photo to brick set conversion with 3D preview and parts list
Workbench for collaborative brick-by-brick editing
MCP server integration with Claude Code, Codex, Cursor and more
Build structure checking and validation
OAuth sign-in with no token storage
Offline build access with local browser storage
Export digital manuals and wanted parts lists
Interactive environments for AI training
Consent-based human demonstration recording
What makes it different
Built by creators of Ray, the world's most widely adopted AI compute engine
Multi-cloud deployment without code changes
Feels local but runs distributed
Unified GPU pooling across clouds and regions
Checked outcomes - part connections and build validity are verified by software before delivery
Real objects from photos - converts pictures into buildable brick sets with actual parts that exist
People and AI work on the same design simultaneously within the workbench
Measurable AI learning with transfer testing across different worlds