DIGITAL CRITICAL INFRASTRUCTURE
Sovereign AI for the sectors that cannot let data leave the perimeter
Defence, government, police and first responders, utilities, telecommunications, financial services and healthcare share one constraint: the data is too sensitive, too regulated or too classified for an outside AI service. We build for that constraint. Pick your sector to see the problem, the platform's answer and the rules that apply.
Pick your sector
Each sector page describes the problem in that sector's own terms, the outcomes the platform delivers there, and the sub-sector pages that go one level deeper.
The same refusals in every sector
The sectors differ in vocabulary and regulation. What they need from the platform does not, so it is built once and refuses the same things everywhere.
Nothing leaves the perimeter
Agents ask before they act
Evidence by construction
Regulation by sector
The frameworks each sector is measured against. Every page lists what the framework asks and which platform controls answer it.
Keep an eye on this space
What changed for your sector in European AI sovereignty, and what we learned in the field. A few times a year, no drip campaign.
Prefer to write? Email hello [at] scrydon.com and we will get back to you.
Keep going
Three routes from here: the rules you will be measured against, the platform these outcomes run on, and the sessions where we walk through them.
The rules that apply
Each regulation, what it asks, and the controls the platform provides for it.
The platform behind it
The AI Operating System, Analytics and Sovereign Foundations, page by page.
Every use case
All of them in one place, grouped by the sector that knows them best.
Ground – Beyond the lakehouse: the ontology as the data layer AI & humans can actually use
29 Oct 2026, 10:00
Part 5 of the Sovereign AI series, for CDOs, heads of data and analytics, and data architects — in any sector. The lakehouse gave you governed storage on open formats: table-shaped, read by analysts. Agents, and increasingly analysts, reason over meaning — entities, relationships, state and rules that people, applications and AI read from and write back to. That is an operational ontology, and it is not a semantic layer or a knowledge graph. Live: the same business question answered by vector RAG over documents and by ontology RAG over the model, side by side with provenance; then an agent writing back through the ontology to an operational system under policy. And the migration path from an existing lakehouse — what you keep, what you add, what you never move.
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