Regulated AI Navigator

Turn an AI use case into its EU AI Act risk class, the regulations it triggers, the obligations, the architecture and the evidence you owe — in about two minutes.

Community-curated knowledge graph, peer-reviewed by experts across law, engineering and governance. Every change traceable →

Analyse a use case →Browse 35 profiles

Regulation coverage

Every regulation in the graph resolves into concrete technical components — through an article obligation, a control objective, a design pattern or an evidence artefact that a component must produce. Pick a regulation to see its technical surface, how each component is derived, and in which of the triggering use cases it is actually part of the required stack. The gap counts show where the graph reaches a component that no use case yet requires — those are open contribution targets, not settled answers. Open the full graph →

Digital Services Act

source EUR-Lex

Intermediary services: recommender-system transparency, risk assessments for very large platforms.

5 components0 articles / obligations2 triggering use casesopen in graph

Output Rails / Groundedness Check

100% of use cases

Faithfulness scoring of answers against retrieved sources; deterministic fallback instead of hallucination; schema-validated structured output.

  • practice-derived2 triggering use cases require Output Rails / Groundedness Check

Synthetic-Content Labelling / Watermarking

100% of use cases

Synthetic-content labelling & watermarking: visible disclosure plus machine-readable provenance (C2PA Content Credentials) embedded in generated images, audio and video; metadata identifying artificial origin survives common transformations. Discharges Art. 50(2)/(4) for deepfakes and synthetic media; verification telemetry (watermark presence/validity checks at publication gates) is the corresponding evidence stream.

  • practice-derived2 triggering use cases require Synthetic-Content Labelling / Watermarking