Analysts face thousands of signals daily from disparate sources. No human can synthesize conflict events, macro indicators, sanctions, and news simultaneously.
Most prediction systems produce scores without explanation. Decision-makers cannot verify reasoning or challenge assumptions behind the output.
Without calibration metrics and audit trails, there is no way to measure whether predictions improve over time or if the system actually learns from outcomes.
Conflict events (ACLED, UCDP), news intelligence (GDELT), sanctions (OpenSanctions), macro indicators (FRED), governance indices (V-Dem, WGI) unified into 18-feature vectors.
Every prediction includes calibrated probability P, confidence interval [CI_low, CI_high], time horizon, and ranked key drivers. No black boxes.
7-stage autonomous learning: backfill, retrain, isotonic calibration, walk-forward validation, Monte Carlo fitness, drift detection, atomic activation.
Brier score, log loss, and reliability diagrams computed on resolved outcomes — not backtested simulations. Fully transparent.
Every prediction links to its input snapshot hash, feature vector, model version, and evidence chain. Click any forecast to reproduce it.
Page-Hinkley detector monitors calibration in real-time. When performance degrades, the 7-stage pipeline retrains and validates before activating new weights.
Defence: conflict forecasting, escalation early warning, ISR triage. Civilian: financial risk, supply chain disruption, insurance pricing, corporate geopolitical intelligence.