SIGMA-VANGUARD is an AI-powered image analysis system designed to support military and defense intelligence workflows. It processes static or real-time imagery from drones, ground cameras, or other visual sources to detect and classify military vehicles and aircraft with high precision. By leveraging deep learning and computer vision, SIGMA-VANGUARD identifies and categorizes objects such as trucks, IFVs, APCs, tanks, missile launchers, helicopters, and fixed-wing aircraft.

The system is currently optimized for the Baltic region, reflecting the unique terrain, vehicle types, and visual patterns common to this operational environment. This regional focus enables higher classification accuracy and contextual relevance, ensuring that the intelligence produced is meaningful and mission-ready.

SIGMA-VANGUARD is built to enhance the speed and quality of information available to analysts. Rather than manually scanning hours of footage or satellite captures, analysts receive pre-tagged visual insights that streamline the evaluation process. This allows human experts to concentrate on strategic assessment, decision-making, and response planning, rather than initial identification.

While in limited deployment today, SIGMA-VANGUARD continues to evolve with the help of operational feedback and regional data. Its modular design allows for future expansion into other regions or domains, making it a scalable tool for modern defense intelligence operations.

Current Status: In Training Phase

SIGMA-VANGUARD is currently in the active model training phase. Our team is refining object detection and classification capabilities using region-specific datasets to ensure reliable, high-accuracy performance in real-world conditions.

At this time, the system is not yet available for production use. We are working closely with select partners and early testers to validate and improve results. Broader access and deployment will be announced as we reach key development milestones.

List of models

Model	Version	Status
sv-bal	1.0b	In training