Sim, digital twin, world model—every model deployed in the physical world is a prior. Nervous Machine calibrates it against runtime telemetry on-device, with certainty-scored assurance flags. Raw data stays on-site; calibrated flags cross organizational boundaries.
Per-edge causal learning, applied to three different physical environments. Each case study runs on a real public archive, with the methodology fully published.
A 106-day audit of IBM's ibm_fez Heron processor. The substrate discovered clusters with no coupling-map input and no vendor hints; 15 of 17 were confirmed across multiple coordinated-motion events.
A 2.5-year offline causal audit across five telemetry owners. Eight cooling edges classified into a vulnerability taxonomy; one non-owned edge surfaced that no single silo could observe; a drift pattern that silently invalidates control laws while forecasts look healthy.
Per-edge causal learning across four space-environment regimes against real archive ground truth from GRACE-FO, GOES-19, MSL/RAD, and ARTEMIS-P1/P2. Calibrated certainty plus the discovery of two unmodeled physical phenomena the priors didn't encode.
Nervous Machine takes your design-time prior—sim output, digital twin, world model—and calibrates it continuously against runtime telemetry. No retraining. No cloud roundtrip. Just on-device certainty calibration.
Bring your existing prior—sim output, digital twin, world model, domain literature. The system starts from the best available design-time knowledge, then calibrates it against deployment.
Runtime telemetry captures actual outcomes. The substrate exposes exactly where the prior diverges from deployment. No labels needed—just ground truth.
Agile where ignorant, stable where confident. The system knows what it doesn't know and adapts accordingly.
The system autonomously flags knowledge gaps and surfaces hypotheses instead of masking them. Unknown unknowns become first-class signals.
Calibrated learning propagates across the fleet at a tiny footprint. Raw telemetry never leaves the device. Every node benefits from collective certainty without sharing sensitive data.
The loop runs continuously on operational data. Models improve with every measurement cycle. Learning is architecture, not a scheduled retraining job.
The framework is lightweight enough to run on an MCU and robust enough to orchestrate fleet-wide learning across thousands of devices. No GPU cluster required. No cloud dependency. Deploy where the physics happens.
Satellites, factories, robots, data centers, chips. Anywhere a model is deployed in the physical world, Nervous Machine calibrates it against what's actually happening.