Machine learning that trains where the data lives. On drones, vehicles, and industrial fleets. Raw data never leaves the device.
Winter Demo 2026 | BAE Systems Bofors
Scaleout enabled autonomous drones to learn and operate in contested Arctic environments without central data links.
Real-time AI model distinguishes multiple target types locally during autonomous search flights.
Onboard prioritization logic selects targets based on mission requirements and real-time environmental data.
Full autonomy without reliance on external processing or continuous radio links for flight path adjustment.
Scaleout Edge Platform
The AI systems that matter most run where data cannot be centralised and networks cannot be trusted. Scaleout Edge manages the complete model lifecycle in those environments — from federated training and versioned deployment to continuous monitoring and retraining.
Models train on-device. Only encrypted weight updates are shared. Your data stays exactly where it is, enforced by architecture.
Every version recorded with its compute package hash and session lineage, creating a complete audit trail across the fleet.
Training metrics and hardware telemetry from every node. Visibility where you have no physical access.
Extends your existing ML stack to handle distributed model training and management across devices.
Tactical CV Network
Static models degrade as environments change and new threats appear. The operational footage that would fix them can't be centralised — classified, bandwidth-constrained, sovereignty-bound. Tactical CV Network closes the loop without moving the data.
Source: rtmpConn
Counter-UAS, ISR, and ground surveillance — single platform, unified operational picture.
Models retrain on live sensor data. Raw footage never leaves the site.
Learning aggregates across every node in the network when connectivity allows.
Detections flow into ATAK and standard C2 systems via TAK, REST, and MQTT.
Use Cases
From contested defense environments to regulated industries, a consistent pattern: distributed data that cannot move and AI that must keep improving.
Drones, unmanned systems, and forward-deployed sensors operating in contested or bandwidth-denied environments.
Thousands of vehicles generating terabytes of telemetry daily — too much to centralize, too valuable to ignore.
Remote mines, railways, and energy infrastructure where data sovereignty is a legal requirement.
High-value datasets in government and healthcare siloed by privacy law and classification.
Technical publications and perspectives from the Scaleout team.
Autonomy is not a single AI model; it is a tightly coupled system of hardware-agnostic modules. Our stack transforms raw visual input into structured mission logic, enabling drones to maintain persistent target memory and execute complex maneuvers in communication-degraded environments.
Sigvard Dackevall
Machine Learning Engineer
We tested privacy risks against production-grade vision models like YOLO and ViT. In realistic settings, meaningful image reconstruction often collapses.
Viktor Valadi, Mattias Åkesson, et al.
Test your models against 10,000 drifting, dropping, and reconnecting clients. Scaleout Edge provides the modular infrastructure to move from lab prototypes to high-concurrency production deployments.
Scaleout