AI gets better with more data. The data that matters most is data you can't move.
If you can't send the data to the cloud, send the model to the data.
Trusted by leading organisations
Training moves to the edge. The data stays in place.
Most edge AI ships a finished model to the device and lets it degrade. Scaleout Edge keeps the whole training loop running in the field, so the model sharpens on real conditions instead of drifting from them.
Speed
A pattern learned at one node reaches the whole fleet within the same operational window, not the next release.
Accuracy
Models train on real local conditions (that site, that season), not a centralised set that only approximates them.
Reach
What one node learns, every node gains: collective intelligence built from encrypted weight updates, never shared raw data.
Resilience
No persistent uplink required. A disconnected node keeps inferring, capturing, and training. Nothing is lost; it syncs on reconnect.
A continuously improving CV capability for cUAS and ISR.
Vision models that adapt to operational conditions in the field. Without raw footage leaving the site.
Where the technology is being put to work
Across European defence programmes, NATO initiatives, and industry partners.
NATO DIANA Innovator
Selected by NATO's Defence Innovation Accelerator for federated AI in contested environments.
TechSweden Security Award
Presented by Minister of Defence Pål Jonson. Awarded for sovereign edge AI infrastructure deployed in active defence programmes.
BAE Systems Arctic Demo
Autonomous drones learning and operating in contested Arctic environments. No central data link, no raw data leaving the device.
Deploying AI at the edge?
Every deployment has different constraints: data that can't move, networks that can't be trusted, models that must keep improving. Tell us about yours.