DAGR AUTOSINT
DAGR fuses public-source reporting, prediction-market signals, aviation and maritime movement data, and other observable indicators to identify convergence across independent sensors.
Correlates narrative and public reporting, prediction markets, aviation ADS-B, maritime AIS, and other public sensors to identify cross-domain convergence and reinforcing indicators.
Monitors market repricing, anomalies, and changes in implied expectations, then compares those signals against public reporting and observable real-world activity.
Tracks public ADS-B and AIS activity to surface relevant movement, route changes, clustering, loitering, and other observable aviation or maritime patterns.
Specialized AI agents analyze different sensor streams, compare findings, challenge or reinforce assessments, and elevate convergence signals with source-backed confidence.
Detects narrative emergence, amplification, momentum, and shifts across public information environments and compares those changes with other sensor activity.
Surfaces meaningful deviations, accelerations, and emerging patterns across sensor streams so mission teams can identify potential indicators earlier.
Connects findings to source provenance, supporting and contradictory evidence, confidence, and analyst review for traceable decision support.
Turns complex multi-sensor activity into concise outputs centered on what changed, what supports it, why it matters, and what conditions to watch next.
Delivers structured findings into existing government analytics, agentic intelligence, and command workflows without requiring replacement of current systems.
Provides the tools, mission-specific configuration, integrations, and qualified personnel or analyst support needed to deliver an operational capability, not just software access.