One framework,
several applications.

2,400+ nodes across 195 countries. 49,000+ edges. 21 live data sources spanning health, conflict, economics, energy, climate, food, displacement, marine, trade, and space.

The Everything Engine monitors the structural topology of global interdependence in real time. When the graph begins to show critical slowing down - elevated AR1, rising variance, compressing Fiedler eigenvalue - crisis is weeks away.

Replayed blind against 37 years of history: 21 of 25 catalogued events detected, most with 6-12 weeks of lead time. All four misses are documented, including one we chose not to recover because doing so would have cost roughly 14,000 false detections.

The Structured Intelligence Platform deploys edge sensors that build an in-memory graph of network connection topology. Anomalies are detected when graph metrics - not traffic content - deviate from baseline. Zero-knowledge by architecture.

SIET extends this to enterprise telemetry. It learns what each part of the estate normally does and raises a finding when something departs from its own history, not when a rule matches.

Measured on CICIDS2017 against a frozen benign baseline: every attack detected with no signatures and no attack knowledge, median time to detect under one minute, and 3 cases on a benign control day. Every attacked host was reported: 19 of 19, 18 as cases and 1 as a lower-tier observation. Port scan, DDoS, DoS, brute force, infiltration and C2 classes all caught.

~700 UK and US equities monitored continuously. Every 15 seconds, a correlation graph is rebuilt and structural metrics computed. When the topology shifts - when density compresses, entropy falls, centrality concentrates - something is accumulating.

The same Scheffer indicators that detect a pandemic 11 weeks out detect the structural preconditions of a major equity move. The mathematics is the same. The domain is different.

Backtested to 67% win rate on ACCUMULATION structural breakout signals. Annualised Sharpe of 10.64 and Sortino of 46.62. Validated in paper trading before live deployment.

Neural networks undergo phase transitions during training - grokking, catastrophic forgetting, mode collapse. These are not random. They are structural. The weight graph Laplacian carries the signal weeks of training steps in advance.

Neural Structured Intelligence applies Scheffer early warning indicators to the spectral properties of the weight graph. AR1 and variance ratio computed on Laplacian eigenvalues. When these metrics indicate critical slowing down, a phase transition is imminent.

Validated across five sequential experiments: grokking detection with 21,000-step lead time, classification of forgetting vs grokking (3.7× faster λ₂ divergence), active steering with 91.7% knowledge retention, compounding across three tasks, and preemptive curriculum design.

Fusion plasma is a dynamic system. Disruptions - sudden, catastrophic losses of confinement - are the primary engineering risk in tokamak operation. On ITER, a single disruption could damage the first wall.

Using the FAIR-MAST public dataset from UKAEA's MAST tokamak, we applied the Structured Intelligence framework to multi-channel plasma diagnostic time series. Disrupted plasmas exhibit sustained elevated Fiedler eigenvalue throughout flat-top - a structural predisposition detectable from the start of the shot, not just in the precursor phase.

F1=0.714. Recall=1.000. Mean lead time: 49% of flat-top remaining (~153ms absolute). On ITER geometry, this would provide approximately 5× the required 30ms response window.