Zero-Day Detection Capability
100% Data Coverage
Multi-Anomaly Detection
Technical Research

TechnicalWhitepapers

Each study applies the same engine to a different domain - and discovers behavioral patterns that weren't previously visible.

Geographic Contagion in U.S. Housing Markets

Real-Time Detection on 932 Metro Areas

Housing stress spreads geographically, and now we can see it coming. This study discovered 91,973 leader-follower relationships between U.S. metros, providing a 1.7-month early warning before stress propagates. Investors, lenders, and policymakers finally have a map of how corrections travel.

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Predicting Pitcher Injuries Before They Happen

84.2% Detection Rate with 24-Day Warning

MLB teams lost $347 million to arm injuries in 2025. This study detected pre-injury mechanical changes in 16 of 19 pitchers, averaging 24 days before they hit the IL. The key finding: injuries announce themselves through subtle multi-dimensional shifts that human observation misses.

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Signal Clustering Predicts Market Volatility

98.9% Confidence - Same Pattern as Pitcher Injuries

Individual anomalies are noise, but when they cluster together, major events follow. Bitcoin signal clusters predict volatility 2.8x better than random (p=0.011) with 36-day average lead time. The breakthrough: this is the same pattern that predicts MLB pitcher injuries at 84% accuracy. Complex systems under stress cluster their warnings before failure.

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Two-Pass Fleet Behavioral Analysis

Adding Context Reveals Anomalies, Not Explains Them

A surprising discovery: adding position and tire context to fleet analysis found 3x MORE anomalies, not fewer. Behaviors that looked "normal" were actually contextually inappropriate - a P1 car conserving tires SHOULD be pushing. The two-pass architecture separates "unusual for ITSELF" from "unusual for PEERS given situation." Directly applicable to autonomous vehicle fleets, trucking, and rail operations.

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Humanoid Robot Motion Regime Detection

930 Hz Real-Time Processing - 31x Faster Than Sensor Rates

The robotics industry faces a critical gap: detecting behavioral degradation without labeled failure data. This study processes 43-dimensional humanoid motion at 930 Hz - fast enough for "machine reflexes" that respond before the central controller knows something is wrong. Task-specific drift signatures (+77% for manipulation, +20% for locomotion) enable predictive maintenance before component failure. Zero training data required.

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