AI
NATO-backed startup deploys lightweight AI to enable autonomous drone combat
A Swedish company is bringing compact machine learning models to military drones, allowing them to identify and strike targets without relying on distant servers.
Ars Technica
A NATO-supported technology company is equipping unmanned aircraft with streamlined artificial intelligence systems capable of detecting and engaging targets in the field. The innovation represents a significant shift in how modern militaries are operationalizing AI on the frontline.
From commercial logistics to military applications#
Scaleout Systems, founded by researchers from Uppsala University in 2016, initially built tools for deploying machine learning directly onto hardware embedded in commercial vehicles and logistics infrastructure. The 2022 Russian invasion of Ukraine prompted the company to redirect its expertise toward defense capabilities, recognizing the strategic importance of moving AI processing closer to sensors and troops in active conflict zones.
"This technology can be very important to operationalize edge data and sensor data for machine learning to make sure that NATO allies have found that strategic advantage," according to the company's cofounder and CEO.
Fitting AI onto constrained hardware#
Rather than relying on large-scale foundation models from companies like OpenAI or Anthropic, Scaleout focuses on smaller, more efficient machine learning architectures. These compact models can run computer vision tasks directly on the limited computing power available in drones, field tablets, and forward command stations—hardware that varies dramatically in capability.
The approach sidesteps the latency and vulnerability of sending drone feeds back to distant servers for analysis, enabling faster decisions on the battlefield.
NATO's accelerator program#
Scaleout joined NATO's Defence Innovator Accelerator for the North Atlantic (DIANA) Challenge Program in 2025, where it has contributed to the Federated Aerial Intelligence for Recon initiative. The project focuses on optimizing machine learning models specifically for the computing constraints of airborne platforms and ground-based command systems operating in contested environments.
