Features
Continuous post-training of agents from real work traces
Signal extraction from outcomes, policies, and expert judgment
Custom reward functions and evaluation metrics
Reinforcement learning to tailor agents to workflows
Memory architecture for compounding learning
Production feedback loop routing outcomes back to training
Evaluation against real constraints and edge cases
Integration with existing tools and data pipelines
Collaborative research and development with Monte team
Custom model fine-tuning and adaptation
GPU compute with NVIDIA Vera Rubin, GB300, B300, Blackwell, Hopper, Ada Lovelace
CPU compute and bare metal servers
AI Object Storage with zero egress migration
Distributed file storage and dedicated VAST Storage
Backblaze multi-exabyte storage integration
Managed Kubernetes with automated provisioning
SUNK runtime acceleration for reinforcement learning
MLPerf v5.0 leading training and inference performance
CoreWeave Sandbox for model and agent development
Mission Control for observability, security, fleet lifecycle
Tensorizer for model optimization
Cluster Health Management and monitoring
Node lifecycle controller for automated node management
High-performance networking for cluster scale-out
Capacity plans with guaranteed compute and pricing
