Trusted autonomy for industrial operations built on machine teaching
Stage
Not given
Not given
Founded
Not given
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Based in
US
US
Pricing model
Paid
Contact for pricing
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
60+ tools for data to autonomy
CLI and API access
MCP server for autonomous coding agents
Flexible compute deployment on cloud, on-premises, or edge
Dedicated supercomputer cluster infrastructure
12-week deployment timeline
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
Built on machine teaching methodology for systems that learn from human expertise
Proven ROI with documented industrial results up to $1.7M per year
Data never leaves your walls with on-premises deployment options
No-code platform for enterprise deployment without extensive technical expertise
AMESA