---
title: AI & RAG Consulting for Government and Public Bodies
source: https://rubradigital.com/industries/public-sector
site: Rubra Digital
description: AI systems for public administration built for transparency, data sovereignty and the accountability standards citizens and oversight bodies expect.
updated: 2026-07-10
regulations: EU AI Act, GDPR, National algorithm registers, SecNumCloud / national sovereignty frameworks, Public procurement directives, Freedom of information regimes
---

# AI & RAG Consulting for Government and Public Bodies

**Summary:** Rubra delivers AI and retrieval systems for government departments, agencies and public bodies across Europe and Canada, designed around data sovereignty, algorithmic transparency obligations, procurement requirements and the explainability that public accountability demands.

## Where I see this working

- **Legislation and policy retrieval.** Search across statute, guidance, parliamentary material and internal policy, with authoritative versioning and effective dates attached to every result.
- **Citizen enquiry support.** Grounded answers for front-line staff handling public enquiries, citing the guidance the answer comes from so the citizen can be pointed at it.
- **Case file summarisation.** Summaries for caseworkers over large files, with the human decision explicitly retained and the summary always traceable to source.
- **Procurement and tender analysis.** Retrieval across tender documentation, prior awards and framework agreements.
- **Internal knowledge continuity.** Making decades of institutional guidance findable as experienced staff retire.

Public sector AI work carries a constraint private sector work does not: the
people affected by the system did not choose to interact with it, and cannot go
elsewhere. That changes what an acceptable error rate means, and it changes how
much of the system's reasoning has to be visible.

## Design principles I hold to here

**Decision support, never decision making.** Where an outcome affects a citizen,
a human makes it, sees what informed it, and can override it. The system's job
is to bring the right material to that person's attention.

**Explainability that a citizen could follow.** Not a feature importance chart,
but a plain statement of which documents informed an answer, in language a
member of the public could read. Retrieval systems are well suited to this,
because the citations are the explanation.

**Sovereignty settled before architecture.** EU-only, nationally qualified, or
fully on-premise are different systems, not different deployment settings.

**Built to be published.** Assume the evaluation results, the limitations and
the design will be disclosed. Document accordingly.

## Where public bodies get the most value

Consistently, the least controversial use cases: making existing guidance
findable. Departments hold decades of policy, precedent and internal
interpretation that new staff take years to learn and retiring staff take with
them. Retrieval over that corpus improves consistency of decisions without
touching the decisions themselves. It is also the use case where oversight
bodies raise the fewest objections, because nothing about the decision process
changes except how quickly the right guidance reaches the person making it.

## Frequently asked questions

### Can AI systems be used where decisions affect citizens?

With significant constraints, and often as high-risk systems under the EU AI Act. Several categories are explicitly high-risk under Annex III: access to essential public services, benefits eligibility, law enforcement and migration. In practice I build these as decision support with the human decision preserved and documented, never as automated decision making. Several member states also maintain algorithm registers with their own publication requirements, which need to be part of the plan from the start rather than discovered at launch.

### How do you meet data sovereignty requirements?

By designing for the constraint rather than working around it. That can mean EU-only hosting and inference, nationally qualified hosting such as SecNumCloud in France, or fully on-premise deployment with open-weight models where no external inference is permitted. The architectural decisions differ substantially between these, so the sovereignty requirement has to be settled before design begins.

### What about transparency and freedom of information?

Public bodies are generally subject to disclosure obligations that private organisations are not, so I assume from the start that the system’s design, evaluation results and decision logic may become public. That assumption improves the engineering: it forces clear documentation of what the system does, what it does not do, and how well it performs. Systems built on the assumption of scrutiny tend to withstand it.


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Source: https://rubradigital.com/industries/public-sector
Rubra Digital. Independent LLM and RAG consulting for regulated and document-heavy organisations in Europe and North America.
Contact: hello@rubradigital.com