Multiverse Computing partnered with a national defense champion to deliver a sovereign, on-premises object detection capability for aerial asset monitoring. The project compressed a state-of-the-art detection model into a footprint that runs efficiently on defense-grade edge GPU hardware deployed inside the client's own infrastructure, with no dependence on external clouds, and was validated end to end through our five-phase optimization pipeline.
The Challenge
The client required a sovereign, on-premises AI capability for detecting aerial assets in imagery, runnable on edge GPU hardware deployed inside its own infrastructure. The goal was to drastically reduce model size and computational load while preserving operational detection accuracy, and to validate the result on defense-grade hardware. Standard, uncompressed detection models were too heavy for the deployment footprint and too dependent on cloud or central compute to fit the sovereignty and security requirements of the mission.