Nervous Machine

Company Overview

Nervous Machine provides a deterministic, first-principles mathematical protocol designed to map the boundaries of system knowledge and enforce a verifiable safety envelope at the edge. Operating entirely on-device with zero GPU, cloud, or neural-network dependencies, our kilobyte-scale kernel acts as a real-time runtime verification layer for complex robotics, unmanned aerial systems (UAS), and distributed satellite constellations.

Core Capabilities

• Runtime Assured Autonomy (RTAA) via Causal Envelopes: Act as a deterministic RTAA safety filter for un-vetted control and reinforcement learning (RL) policies. Instead of using brittle numerical thresholds that cause false flags, the kernel monitors the structural validity of physical relationships in real-time, mathematically isolating operational drift or malicious signal manipulation before it propagates to actuation.
• Hierarchical Prior Inheritance & Constellation Mesh Seeding: Engineered a modular transfer architecture for robots, drones, and satellite constellations. When a new policy or orbital asset is deployed, it inherits a structured causal prior (~50kb). To guarantee safety, inherited edges seed at low operational certainty (0.30–0.40), forcing the system to rapidly re-earn certainty against local physics before executing high-risk maneuvers. Critical rules-based edges seed at high certainty (0.90) to minimize learning-rate drift while keeping the safety envelope secure.
• Ultra-Low SWaP Execution (Kilobyte-Scale): The protocol is compiled entirely to Rust in the kilobyte range. Causal vectors are compressed down to ~1kb per edge, allowing real-time telemetry mesh synchronization across highly constrained downlinks, distributed robotic swarms, and embedded ARM targets (Raspberry Pi/Jetson-class) without clogging bandwidth.
• Introspective Re-Hypothesis & Gap Registration: When encountering low-certainty anomalies, the kernel executes autonomous re-hypothesis loops against external domain knowledge to patch the causal graph locally. Persistent, unresolvable anomalies are cleanly logged to a definitive Gap Register, defining the exact mathematical boundaries of what the system does not know to inform human operators or future sensor placements.

Proven Validations & Dual-Use Case Studies

• Aerospace & Cybersecurity (ESA Swarm Constellation): In a stress test on real ESA and NASA data through the G5 Gannon superstorm, isolated an injected spoofing signature 13× smaller than the surrounding storm noise with zero false flags, demonstrating edge-level cyber-resilience under extreme operational stress.
• Space Domain Safety & Navigation (Space Atlas): Mapped 41.5 million in-orbit measurements across 131 orbital regions from LEO to the Mars surface, establishing a definitive "map of useful space" and flagging space-weather storm model failures in real-time at 92% precision.
• Quantum Telemetry Auditing (156-Qubit Processor): Discovered 17 hidden shared-resource cross-component clusters from telemetry alone, with zero coupling-map input or vendor hints; 15 confirmed across multiple coordinated-motion events.
• Digital Twin Assurance (CINECA m100 Supercomputer): Published a certainty map of a supercomputer's telemetry with predictions frozen and hashed before the twin's first gradient step. The map rank-ordered the digital twin's 2022 failures at ρ = +0.94, while the twin's own ensemble variance localized nothing.

Technology Readiness Levels (TRL)

• TRL 6 — Space Domain Cyber-Resilience & Anomaly Isolation: Validated on real on-orbit telemetry from the ESA Swarm constellation, isolating an injected spoofing signature from extreme G5 geomagnetic noise with zero false flags.
• TRL 5 — Runtime Assured Autonomy (RTAA) Core Math Kernel: First-principles Rust kernel compiled and validated on hardware targets within high-fidelity simulated flight environments (USAF RTAA SBIR proposal submitted).

Corporate Information & Readiness