Most AI optimizes for the mean. Nervous Machine learns your ecosystem—your machines, your configurations, your environment—in real time, at the edge, without constant cloud inference.
Nervous Machine enriches population-level priors with instance-level causal models that update from operational reality. No retraining. No cloud roundtrip. Just continuous calibration at the edge.
Build an initial causal graph from domain literature, simulation outputs, or digital twin priors. The system starts with the best available knowledge—then improves it.
Sensors capture actual outcomes. Pure error signals (predicted vs. actual) expose exactly where the model diverges from reality. No labels needed—just ground truth.
Learning rate is inversely proportional to certainty. Agile where ignorant, stable where confident. The system knows what it doesn't know and adjusts accordingly.
High certainty + persistent error = missing causal factor. The system autonomously flags knowledge gaps and generates hypotheses instead of masking them.
Validated causal vectors (~1KB) propagate across the fleet. Raw telemetry never leaves the device. Every node benefits from collective learning 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.
Per-install or subscription. Start with a single device and scale to fleet-wide intelligence. No lock-in.
Single-device causal learning. Your machine learns its own signature from operational reality.
Multi-device propagation. Validated causal edges shared across nodes without exposing raw data.
Bespoke causal model training for your domain. We build the initial graph with your physics and your data.
Satellites, factories, robots, data centers. If it operates in the physical world, we can make its models smarter.