Not based on a specific customer deployment. The company, figures, decisions, and outcomes below are a constructed example that shows how Intended’s mechanism applies to this kind of problem. They are not measured results from a named customer. Where we publish a real, attributed customer outcome, we will say so explicitly.
Mapping AI Governance to the EU AI Act in the Public Sector
Consider a public-sector agency whose AI agents handle document processing, case management, benefits eligibility determination, and citizen communication — operating under transparency and data-residency obligations such as the EU AI Act.
01 · the challenge
The kind of problem this addresses.
02 · how it works
See the difference.
Citizen submits benefits application
Application #FA-2026-48291
AI agent evaluates eligibility
No transparency into decision logic
Decision rendered
Denied -- no explanation provided to citizen
No audit trail
Cannot reconstruct why AI denied application
03 · the solution
What they deployed.
- — Installed Government Operations domain pack with citizen service and case management intents
- — Configured EU AI Act compliance mode: mandatory human review for adverse decisions
- — Data residency enforcement: all Intended processing within sovereign infrastructure
- — Citizen-facing decision explanations generated automatically from authority traces
- — Immutable audit records with government-grade cryptographic signatures
04 · implementation
From zero to governed.
Phase 1
Assess
Map AI agent decision points against EU AI Act requirements. Identify the high-risk AI use cases that require governance.
Phase 2
Deploy
Install the Government Ops domain pack on sovereign infrastructure. Configure data-residency controls and citizen privacy rules.
Phase 3
Integrate
Connect case management, benefits, and document-processing systems. Train caseworkers on escalation workflows.
Phase 4
Evidence
Generate evidence demonstrating transparency, explainability, and human oversight for adverse AI decisions to support a compliance review.
05 · illustrative outcomes
What this is designed to deliver.
Modeled figures for this scenario — what the workflow above is built to achieve, not measured results from a named customer.
0%
Adverse decisions human-reviewed
By design, per EU AI Act mandate
0%
Decisions explainable
Citizen-facing explanation from the authority trace
0%
Data residency enforced
Processing stays in sovereign infrastructure
0-click
Evidence export
Maps to EU AI Act control requirements
06 · decision replay
Example decisions, full trace.
Sample decision records that show the shape of the evidence Intended produces. Illustrative, not drawn from a live customer’s logs.
gov.citizen.benefits-determinationRISK: 76/100ESCALATE44msAI evaluates housing assistance eligibility for Application #FA-2026-48291
Resolved by: Senior Caseworker (reviewed and confirmed denial in 12m)
gov.document.classificationRISK: 22/100ALLOW31msAI classifies submitted tax documents for verification
Resolved by: Policy: document classification auto-approved (non-adverse)
gov.citizen.communicationRISK: 34/100ALLOW28msAI generates response letter to citizen inquiry about application status
Resolved by: Policy: informational communications auto-approved with template check
gov.citizen.data-exportRISK: 100/100DENY15msAI agent attempts to export citizen PII to analytics system outside jurisdiction
Resolved by: Policy: data residency violation -- target system outside sovereign boundary
the takeaway
The EU AI Act requires that citizens can understand and challenge AI decisions that affect them. Mapping authority traces to that requirement provides explainability and human-oversight evidence without rebuilding the underlying AI infrastructure.
Why this pattern matters — not a customer quote.
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