Nervous MachineYou measure it.
Nervous Machine attaches a calibrated certainty to every cause-and-effect link in a system earned from real measurement, in place, without retraining. Then it debriefs your team: what holds, what doesn't, and what to measure next. Simulations and digital twins are blind in shifting environments with conditions they weren't trained on. We help draw that boundary and catch anomalies before they become failures.
DEBRIEF is how a team hears from a system that has been learning while they were elsewhere. On a schedule, or right away when something can't wait, it walks the team through what was learned, what was refuted, what is still open, and what needs a person. Then it takes questions.
Scripted prototypes. Narration is drawn from each mission's recorded ledger; the satellite is simulated. A voice version your team can question is in prototype.
Accepted means it passed a test written down before the data, or an independent source confirmed it. Refuted means it did not stand. Certified is the higher bar: accepted, and certainty earned. A debrief says which one applies, every time.
A learning run keeps a ledger: each cycle's question, the rule it had to pass, and what the test showed. DEBRIEF answers from that record. When the record doesn't cover a question, it says so, and says what would answer it.
What the system can't close alone is handed to the team as a specific ask: the measurement, the data access, or the expertise that would move it. This is where your people matter most.
Nervous Machine keeps a living map of causal drivers in a system. Every link on that map carries a certainty score between 0 and 1, and the score is earned by making predictions, measuring what actually happened, and learning from the difference.
In-cycle, on-device, kilobytes not terabytes, and no retraining. Certainty is never assumed from simulation. It is measured outside the model, against the world. Whether learning science or the inner workings of a machine, the kernel uses the same math.
High certainty · high error
The model is stable. The system changed. Act.
Certainty that won't climb
Something isn't being measured. Instrument it.
And when a gap survives every hypothesis, it isn't smoothed over or fit by force. It's entered in the gap register: a dated prediction with the missing measurement named. The boundary of your knowledge is emitted as coordinates, magnitude, date, and what would move it.
Use the best available knowledge from domain expertise, LLMs, simulations, and digital twins to initialize the model. These outputs are the best possible guess about causal drivers and their impact within a system. The model is typically ~50kb serialized, and can be deployed to Raspberry Pi, Jetson-class, or cloud targets.
Once deployed the model learns from real-world feedback, refining its understanding and improving its predictions over time. kilobyte-range updates can be sent to a fleet of devices, allowing for rapid adaptation and continuous improvement across the system. The framework can be used to flag anomalies, identify drift or blind spots in simulations and twins, and provide assured autonomy in critical systems.
Edges that exhaust all hypotheses without earning certainty are registered as gaps, with the missing measurement named and dated. The register is the record of unknowns and the boundary of knowledge, and can be used to prioritize future research or training, and inform decision-making in complex systems.
Real public data with pre-registered predictions and earned certainty. Every prior learns from forward prediction and no look ahead, not historical analysis.
ATLAS · Atlas of Useful Space
A map of where knowledge of the space environment is earned — and where it runs out. 41.5 million in-orbit measurements across 8 years; 131 orbital regions assessed from LEO to the Mars surface; storm-model failure flagged in real time at 92% precision; every gap named alongside the measurement that closes it.
MSL-2024 · Mars surface radiation
43,044 measurements, 9 learning cycles, 3 refused hypotheses, and a persistent dose deficit registered as a dated gap. Continued hypothesis cycles against independent archives found two uncatalogued solar-wind arrivals at Mars, verified in MAVEN's plasma and magnetometer archives under detection criteria fixed before the data landed, the two instruments agreeing within an hour. Now registered for the field to confirm or refute.
SWARM-2024 · Three satellites, one superstorm, one spoofed node
Real ESA and NASA data through the G5 Gannon superstorm. Zero false flags through a 600 nT storm, a spoofed signal thirteen times smaller caught in 0.8 hours, and a peer force-fed a groomed lie shed 93% of it, because certainty never transfers between platforms. It is re-earned against local reality.
HERON-156 · Quantum processor telemetry
A 106-day audit of a 156-qubit Heron processor: seventeen shared-resource clusters discovered from telemetry alone, with no coupling-map input and no vendor hints. Fifteen were confirmed across multiple coordinated-motion events. This is the cross-component structure that per-component monitors are blind to by construction.
CINECA-2026 · The m100 HPC dataset
A published certainty map of a supercomputer's telemetry, and predictions on where a trained twin would drift and go blind. The map rank-ordered the twin's 2022 failures at ρ = +0.94; the twin's own ensemble variance localized nothing. Two hypotheses missed their frozen bars and entered the register as dated re-tests.
The learning loop is deterministic arithmetic. No model inference in the cycle, no GPU cluster, no cloud dependency. Kilobyte-scale causal vectors are the unit of knowledge shared across a fleet. Structure transfers, certainty is re-earned locally, and raw telemetry never leaves the machine. Deploy where the physics happens.
Bring one system you don't fully trust. We configure the causal model with you, the kernel learns from your measurements, and your team gets a debrief on what holds, what doesn't, and what to measure next.