SEADES applies Model-Based Systems Engineering discipline to a problem that data analytics platforms alone cannot solve: governing the integrity, alignment, and assurance of ISR sensor data across multiple collection platforms.

ISR data fusion today is plagued by inconsistency — sensors on different platforms produce data in different formats, at different fidelities, with different metadata standards. Traditional approaches throw compute at the fusion problem without addressing the underlying governance gap. SEADES provides an automated, model-based framework that aligns multi-platform data at the schema level, enabling complex analytics and assured data assimilation without the manual reconciliation that consumes resources and introduces error.

The result is improved data integrity, automated quality assurance, and secure management of complex sensor data sets — from collection through exploitation.