Abstract: A system is disclosed for generating a common intelligence picture using a modular architecture that automates multi-source intelligence fusion, behavior prediction, and custody tracking. The system receives priority intelligence requirements, extracts tasking parameters using a language model, and generates collection strategies based on feasibility, resource availability, and historical outcomes. Incoming sensor reports are normalized, correlated, and evaluated using predictive models that incorporate pattern-of-life behavior, terrain constraints, and movement feasibility. The system supports tracking of individual and group targets, assigns behavior alignment and identity confidence scores, and triggers alerts for anomalous or evasive behavior. Custody records are continuously maintained and updated, with reacquisition workflows initiated when identity continuity degrades. Collection plans are refined in real time based on predictive scoring and tasking feedback.

Abstract: A system is disclosed for generating a common intelligence picture through latent space fusion and threat forecasting. Priority intelligence requirements are processed using language and machine learning models to generate structured tasking and prioritize multi-modal sensor collection. Data from geospatial, cyber, radio frequency, and behavioral sources is embedded into domain-specific representations and fused into a unified latent space. The system models behavioral trajectories, detects anomalies, and maintains digital twins of entities and regions. Deviations from expected behavior may trigger alerts, causal inference, attribution hypotheses, and deterrence simulations. Adversary behavior is forecast in latent space, enabling evaluation of intervention strategies with impact and timing assessments. Visualizations, alerts, and reports are generated based on latent divergence and simulation outputs.

Abstract: A system is disclosed for generating a common intelligence picture through latent space fusion and threat forecasting. Priority intelligence requirements are processed using language and machine learning models to generate structured tasking and prioritize multi-modal sensor collection. Data from geospatial, cyber, radio frequency, and behavioral sources is embedded into domain-specific representations and fused into a unified latent space. The system models behavioral trajectories, detects anomalies, and maintains digital twins of entities and regions. Deviations from expected behavior may trigger alerts, causal inference, attribution hypotheses, and deterrence simulations. Adversary behavior is forecast in latent space, enabling evaluation of intervention strategies with impact and timing assessments. Visualizations, alerts, and reports are generated based on latent divergence and simulation outputs.

Abstract: A target custody platform comprising a data acquisition engine, a data analysis engine, a machine learning engine, and a data presentation layer configured to task a plurality of satellites for imagery data wherein the imagery data and metadata is used in conjunction with other types of data including identification data and weather data as inputs into a one or more machine and/or deep learning algorithms configured to predict a the likelihood a target of interest will travel along a project path.