Six validated domains.
One framework.

2,860+ nodes across 195 countries. 53,000+ 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.

Validated against 10 historical events across 37 years. Consistent 11–12 week lead times. Zero false alarms on events that did not occur.

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 (Structural SIEM) extends this to enterprise telemetry: Windows Security, Sysmon, network flows, cloud events, and database access are unified into a co-occurrence graph. Attacks are detected when the shape of activity changes - not when a rule fires.

Slow-burn attacks, lateral movement, and persistent threats are caught via entity drift analysis across a 90-day baseline - the structural signature that months of activity leaves behind.

~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.