Agentic infrastructure that runs on your hardware.
BitQubic trains models across organizations without moving the data, simulates scenarios before you commit to them, and shifts compute to the hours when the electricity grid is cleanest.
Built to work separately or together.
They share one control plane, so you can run them across different regions, hardware, and legal jurisdictions.
Test a decision before you make it.
Run up to a million agents in a simulated environment. Every run replays exactly, you can fork it at any step, and you can trace any result back to the agents that caused it.
Train without moving the data.
Train one model across several organizations. Raw data stays at each site, and training keeps going when a site drops offline.
Run jobs when the grid is cleanest.
A Kubernetes scheduler that reads live grid carbon data and moves jobs to cleaner hours. It will not break your service-level commitments, and it records emissions for each run.
How a simulation run works.
Regulators, banks, and research teams use it to test a policy, drug, or strategy thousands of times before committing to it.
- Deterministic. The same starting state gives the same result on any supported hardware, down to each agent’s decisions.
- Counterfactual. Fork at any step, change one input, and keep both versions running so you can compare them.
- Attributable. Trace any outcome back to the agents and interactions that produced it.
- Signed. Every run produces a tamper-evident record a regulator can check.
How the pieces fit together.
Agentic runtime
Software kit for Python and Rust. Works with OpenAI, Anthropic, or locally-hosted models.
Federated trainer
Raw data stays on each site. Supports PyTorch and JAX with built-in privacy and resilience.
Carbon scheduler
Kubernetes add-on that reads grid data from Electricity Maps, WattTime, or your own sensors.
See it run on your own setup.
Tell us what you want to simulate, train, or schedule. A solutions engineer replies within one business day.