Modern warfare produces a flood of sensor and intelligence data that must be ingested, fused, and distributed instantly. Existing data pipelines and architectures struggle to handle real-time, multi-source data securely at scale.

Warfare involves incomplete information, ambiguity, deception, and rapidly changing circumstances. While many systems have traditionally depended on the assumption of a well-defined or fully observable problem space, newer architectures need to be dynamic to function effectively under partial observability.

Reinforcement learning agents still mainly learn correlations specific to their training environments instead of discovering true causal relationships. Worldscape creates AI agents specifically for causal reasoning, as understanding causality is essential for success in complex warfighting scenarios.

The Worldscape platform merges the digital and physical worlds to build distributed command and control systems within a collaborative decision-making environment, where human actions are visible to machine learning algorithms and machine actions are visible to humans, promoting an environment where AI and humans work together.

Worldscape’s simulation architecture is scalable and capable of handling large engagements or multiple simultaneous skirmishes across a theater of operations. It is also flexible, allowing for quick reconfiguration using cloud computing and distributed simulation frameworks to serve a critical role.