AI gets better with more data. The data that matters most is data you can't move.
Scaleout Edge manages the full AI lifecycle at the edge, across fleets where data cannot move.
Trusted by leading organisations
The model improves where it operates, without the data ever moving.
Most AI is trained centrally, deployed once, and left to degrade. Scaleout Edge inverts that: models learn in the field and share only encrypted weight updates, never the raw data.
A new pattern at one node retrains on-site and redeploys across the fleet within the same operational window.
Models train on real local conditions (that site, that season), not a centralised set that only approximates them.
What one node learns, every node gains: collective intelligence built from encrypted weight updates, never shared raw data.
No 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.