{"id":13631,"date":"2025-06-19T11:14:26","date_gmt":"2025-06-19T08:14:26","guid":{"rendered":"https:\/\/teaduspark.ee\/?p=13631"},"modified":"2025-06-19T14:40:38","modified_gmt":"2025-06-19T11:40:38","slug":"startup-story-nato-diana-x-scaleout-systems","status":"publish","type":"post","link":"https:\/\/teaduspark.ee\/en\/startup-story-nato-diana-x-scaleout-systems\/","title":{"rendered":"Startup Story: NATO DIANA x Scaleout Systems"},"content":{"rendered":"\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Scaleout Systems<\/strong> is redefining how machine learning is deployed in privacy-sensitive environments. Traditional centralised AI development requires organisations to consolidate sensitive data in a single location\u2014a process that raises significant privacy, security, and regulatory concerns. Scaleout\u2019s software platform solves this by orchestrating machine learning across edge, cloud, and secure environments, bringing computation as close to the data as possible. At the heart of the platform is federated learning<\/strong> – a privacy-first method that enables collaborative model training across distributed devices without transferring raw data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Photo: Katja Hellgren (Machine Learning Engineer) ,Andreas Hellander (CEO & co-founder)<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

How did you come up with the idea to create a startup?<\/strong><\/p>

We had started working on federated learning in our distributed scientific computing group at Uppsala University. We quickly realised that this technology was going to become a foundation layer in the ML stack for applications that require strict control over data ownership and privacy, and we wanted a focused approach to making it operational.\u00a0<\/span><\/p>

What have been the biggest challenges\/failures and the biggest wins so far?<\/strong><\/p>

We were fortunate to engage early with innovators at large enterprises, such as Scania CV.<\/p>

What sets your startup apart from competitors?<\/strong><\/p>

From the beginning, we have had a clear vision for how to bring federated AI to production.\u00a0 <\/span>We are deeply rooted in ML research, but have always stayed focused on evolving the roadmap together with early adopters at mission-critical organisations and enterprises.<\/p>

Why did you choose the NATO DIANA accelerator?<\/strong><\/p>

We had realised what an impact edge AI and federated learning could have for defence applications. Applying to Diana was a natural next step to learn and to evolve our dual-use strategy.<\/p>

Where do you see your startup in 1 year? And where in 5 years?<\/strong><\/p>

In one year, we will be and in 5 years, Scaleout Edge is the go-to platform for mission-critical AI in the edge-to-cloud continuum. We have accelerated progress towards next-level autonomous systems and helped safeguard and scale AI for NATO and allies.<\/p>

Which books\/podcasts\/publications and influencers in your field do you follow and would recommend to other aspiring entrepreneurs?<\/strong><\/p>

Right now, I am reading \u201dCognitive Electronic Warfare\u201d by Karen Haigh and Julia Andrusenko. For startup life, I like listening to the Y Combinator podcast – always entertaining and informative.<\/p>

—\u00a0<\/p>

The Estonian accelerator is implemented by Tehnopol Startup Incubator in cooperation with Sparkup Tartu Science Park.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

\n\t\t\t\t\t
\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Scaleout Systems is redefining how machine learning is deployed in privacy-sensitive environments. Traditional centralised AI development requires organisations to consolidate sensitive data in a single location\u2014a process that raises significant privacy, security, and regulatory concerns. Scaleout\u2019s software platform solves this by orchestrating machine learning across edge, cloud, and secure environments, bringing computation as close to […]<\/p>\n","protected":false},"author":3,"featured_media":13632,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[231,241,243,8],"tags":[],"class_list":["post-13631","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-kaitsetoostus","category-nato-diana-eesti-kiirendi","category-meie-ettevotted","category-uudised"],"acf":[],"_links":{"self":[{"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/posts\/13631","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/comments?post=13631"}],"version-history":[{"count":11,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/posts\/13631\/revisions"}],"predecessor-version":[{"id":13646,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/posts\/13631\/revisions\/13646"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/media\/13632"}],"wp:attachment":[{"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/media?parent=13631"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/categories?post=13631"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/teaduspark.ee\/en\/wp-json\/wp\/v2\/tags?post=13631"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}