The same math reads cardiac arrest, jet engine fatigue, financial collapse, and grid instability. No retraining. No domain calibration. Each vertical is a separate licensing conversation — the framework underneath stays the same.
01 · Aerospace
Every engine fails its own way. The framework reads them all.
Propulsion fatigue, flight systems, and deep-space sensors. 100 of 100 NASA jet engines. 134-cycle early signal.
Validated on NASA C-MAPSS turbofan data with full per-engine resolution. Same math validated on Voyager heliopause sensor crossing — every transition identified at 121.7 AU. The math holds from propulsion fatigue to deep-space sensor states.
100/100NASA engines
134 cyclesEarly signal
$12.6M/yrRecoverable per fleet
02 · Medicine & Healthcare
The body sends a signal before it fails — and before it heals.
Cardiac. Sepsis. Seizure. Athletic injury. Identified across every scale.
Phoenix detects pre-event instability in physiological signals — the same math reads cardiac pre-arrest windows, ICU vital signs falling out of rhythm, neurological state shifts, and musculoskeletal injury approach. Without retraining for each application.
84 beatsCardiac arrest precursor
28 secSeizure onset
20 daysInjury approach
03 · Infrastructure & Energy
No false alarms. Even through a year of healthy operation.
Power grids, pipelines, and structural health monitoring. UK Power Grid · 2.6M data points · Identifies internal build-up. Stays silent on external shocks.
The math reports what's there. It doesn't invent what isn't. Phi identifies internal build-up in engines, hearts, and markets — and correctly stays silent on instantaneous external events like a line cut. Most predictive systems can't claim this.
2.6MData points
0False alarms
365 daysHealthy operation
