Multi-Modal AI System

The Vendor is required to provide are seeking innovative AI (Artificial Intelligence)-driven solutions that fuse heterogeneous multi-domain data streams to provide real-time, explainable, and policy-aware situational awareness for operational decision-making. 
- By increasing situational awareness on the battlefield, this new technology will reduce system vulnerabilities and increase speed of decision making.
- Operations generate vast, heterogeneous data streams such as text reports, imagery, video, audio, Radio Frequency (RF)/signal intelligence, sensor telemetry, and emerging modalities such as quantum-derived measurements and drone-based information.
- These data sources remain siloed, limiting real-time situational awareness and decision-making.
- The capability will enhance agency ability to integrate intelligence across domains and classification levels, ensuring interoperability with allies and secure operations in contested environments.
- Unlike traditional rule-based fusion, this initiative leverages AI-driven architectures to learn complex relationships across heterogeneous modalities, propagate uncertainty, and deliver policy-aware, explainable outputs for mission-critical decisions in dynamic, degraded environments.
- Joint ISR Fusion for Arctic Operations: AI-driven spatiotemporal alignment of satellite imagery, RF signals, and telemetry for persistent Arctic domain awareness.
- Real-Time Threat Assessment in Multi-Domain Battlespace: Deep learning-based fusion of Electro-Optics (EO) video, Signals Intelligence (SIGINT), and text intelligence for dynamic targeting and force protection.
- Edge Fusion for Tactical Units: Deploy constraint-aware AI models on wearable systems to integrate audio, video, and sensor data for soldier situational awareness under degraded connectivity.
- Maritime Task Group Operations: AI-powered anomaly detection using sonar, RF, and visual feeds with uncertainty scoring and explainable outputs.
- Airborne Multi-Sensor Platforms: Fuse radar, Electro-Optics/Infra-red (EO/IR), and telemetry for enhanced detection and tracking of stealth or spoofed adversary assets.
- Solutions should include capabilities and considerations such as, but not limited to, the following:
•    Advanced deep learning architectures for spatiotemporal alignment, uncertainty propagation, and confidence scoring across modalities;
•    Entity resolution and dynamic knowledge graph integration for persistent object tracking across domains;
•    Policy-aware fusion leveraging AI-based provenance tracking for secure integration across classification levels with full lineage;
•    Scalable architecture for real-time AI-powered fusion pipelines in operational environments, including explainable outputs for operator trust; and
•    Incorporate Size/Weight/Power (SWaP) and compute limits into fusion pipelines for edge deployment.