Sovereign AI
Overview
Who it is for. CIOs, CISOs, CTOs and compliance leads in the public sector, financial services, healthcare, critical infrastructure and any organisation whose data, intellectual property or jurisdiction rules out sending everything to a public API.
The problem. Sovereignty is often reduced to "the data stays in the EU". That is residency. Control also depends on who operates the platform, who holds the keys, which models you can inspect and move, and whether you can leave. The rulebook is still forming: the Commission's Cloud and AI Development Act, proposed on 3 June 2026, would introduce four sovereignty assurance levels for public-sector cloud and AI purchases. It is a proposal, not law. Meanwhile teams buy GPUs before they know the workload, or assume an open-weight model matches a frontier model on every task.
What we do. We classify data and AI workloads into sovereignty tiers and map each tier to an eligible platform: a managed API, an EU sovereign cloud region, private cloud, on-prem or a fully disconnected environment. We design and build the serving layer on open-source building blocks - containers, Kubernetes, vLLM-based serving - with a curated catalogue of open-weight models, including Polish-language models such as Bielik and PLLuM where the content calls for them. Every choice is tested on your own evaluation set.
What you get. An architecture you can defend to a regulator, a platform your teams can run, key management under your control and an exit plan for every provider.
How we work
At Keter AI, every engagement moves through the same four layers, in the order of our mark: strategy, architecture, delivery, governance. Each layer ends in something you can inspect and keep: a decision, a design, a working system, a set of controls. The depth changes with the service; the order does not.
Strategy
01
We start from the business problem: which decisions and workflows AI should improve, what that is worth and what must be true first.
Architecture
02
We design the system around your constraints: data boundaries, model strategy, integrations, evaluation and where it will run.
Delivery
03
We build with real data and real users, in short increments, until the system passes its evaluation set and is ready for production.
Governance
04
We leave controls that last: named owners, monitoring, an audit trail, documentation and a clear view of AI Act duties.
Deliverables
Sovereign AI work ends in infrastructure, not in a position paper. The outputs below cover the decision (which workload needs which level of control), the build (a platform that serves models privately) and the proof (measured quality, cost and a tested way out). Each can be commissioned separately.
Technologies we work with
Docker
Containers
Portable, reproducible packaging, so the same AI workload can move between EU cloud, private cloud and on-prem.
Microsoft Azure
Enterprise cloud
Azure AI services for enterprise estates, and Azure Local disconnected operations where systems have to run offline.
GitHub
Code and CI
Repositories, pull-request review and CI pipelines: the place where agent-written changes are tested and approved.
Hugging Face
Open-weight model hub
Hub for open-weight models and datasets. We evaluate, adapt and serve selected models inside private environments.