One address, global access.
Keep your tools and workloads connected to the same data, even as your compute changes.
Use the compute that fits each workload without moving and copying your data every time it changes.
Distributed Training
Robotics & Physical AI
Model Cache & Checkpoints
Agentic Systems
The Problem
MEET SHELBY
Features
Keep your tools and workloads connected to the same data, even as your compute changes.
Make the same data available to more workloads without provisioning and maintaining another full copy for every new environment.
Each upload creates cryptographic evidence of how the data was stored across Shelby.
Use cases
Give agents persistent access to data across sessions, models, and machines with the same interface and a record created at write.
Keep data accessible across distributed pipelines - collected in one location, processed in another and trained in a third.
One origin for weights and checkpoints - reused across supported environments without recreating model state for each one..
Run training across changing compute environments without rebuilding the data layer or adding more staging and copying.
teams behind shelby
One address, global access. Cryptographic record from the start.