Turn an AI use case into its full regulatory footprint — every domain it touches, from AI law and data protection to cyber, product safety and sector rules — with the obligations, the architecture and the evidence you owe, in about two minutes.
Community-curated knowledge graph — every claim carries its citation across law, engineering and governance. Every change traceable →
The business-risk perspective pure compliance review lacks: priority score, compliance cost bands, 3-year ROAI and an explicit verdict. Five-step evaluation pipeline — every threshold, band and formula is read from the knowledge graph (meta.evaluator, v2.21.0) — community-disputable, not hardcoded.
Target market(s)European UnionUnited States (federal)change
Changes the scopeByMarket breakdown in the conclusion below (instruments, horizon, ADM notes per market).
Step 0 — Describe or upload the use case
Service-as-a-Software examples:
Step 1 — Inventory & assumptions
Art. 6(3) derogation criteria (multi):
Step 2 — Regulatory triage
Estimated class: Limited Risk (Transparency)
Effective class after triage: Limited Risk (Transparency)
3-year ROAI: gross annual benefit and recurring annual cost are both discounted at the configured rate for years 1-3; the initial cost (plus regulatory rework reserve) is booked undiscounted at t0.
FAST-TRACK DEPLOYMENT
Automated audit-as-code checks, baseline IT/privacy policy, no extra governance gates. Write down kill criteria anyway.
Indicative decision support, not legal advice. Risk classification depends on your concrete deployment context and can change with scope drift — validate the result with qualified counsel.
Indicative decision support, not legal advice. Risk classification depends on your concrete deployment context and can change with scope drift — validate the result with qualified counsel.