Framework for the generation of synthetic data for machine learning

Purpose and goal

The project aims to adapt and extend Scailab’s synthetic data generation toolchain towards defense and security applications. The goal is to address critical gaps in high-quality training data for AI-based defense systems while establishing a sustainable dual-use business model that supports both civilian and military markets.

Expected effects and result

The project will deliver an extended synthetic data toolchain tailored for defense needs, including new capabilities for defense-related scenarios. Strategically, the team will develop a sustainable dual-use business model, define pricing and commercialization strategies, and establish an IP and compliance strategy aligned with defense requirements.

Planned approach and implementation

During the project, the team will develop and validate Scailab’s synthetic data framework for defense while advancing its dual-use strategy. Activities include stakeholder benchmarking, toolchain enhancement, proof-of-concept implementation, and business, IP, and compliance strategy refinement.

External links

Website for Scailab´s Project: Framework for the generation of synthetic data for machine learning