Bias Testing & Data Quality Pipeline
100% of use casesRepresentativeness checks, bias metrics and mitigation per ISO/IEC 5259; versioned datasets with lineage.
- practice-derived3 triggering use cases require Bias Testing & Data Quality Pipeline
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 →
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 →
First comprehensive US state AI law, effective 30 June 2026: duty of reasonable care for developers and deployers of high-risk AI making consequential decisions (employment, credit, housing, insurance, health); impact assessments, consumer notice and appeal rights.
Representativeness checks, bias metrics and mitigation per ISO/IEC 5259; versioned datasets with lineage.
Computes a probabilistic confidence score for every output and holds the transaction when the score falls below the workflow's regulatory threshold.
Feature attributions for classical ML, reasoning-trace summaries for GenAI — feeds the human reviewer and the technical file.
HITL escalation queue & review UI ('Human-as-a-Tool': the agent calls the human like any other tool via propose-action objects). Confidence- and risk-threshold routing, SLA timers, structured accept/modify/reject verdicts with digital reviewer signature at gate release — each verdict is itself Art. 14 evidence and feeds the active-learning loop.
Operator stop controls and degraded-mode fallbacks; real-time override (veto) channels for HOTL operation.
Escalation triggers built from two independent signals, because raw model confidence is uncalibrated: calibrated trust scores (prompt relevance, similarity to historic successes, cross-model consistency) plus deterministic risk scores (sensitive categories, transaction value, protected data) — either crossing its threshold forces human review.
Append-only, hash-chained audit vault (WORM object-lock storage, AES-256 at rest, TLS 1.3 in transit). Guarantees tamper-evidence within the organization's trust domain — which stops your own team, but not an admin who can rebuild the vault. Pair with an external trust anchor and key ceremonies outside the operating team for evidence that holds against the insider scenario.
Customer-controlled key sovereignty; cascaded encryption independent of the cloud provider.
AMD SEV / Intel TDX: data protected from the cloud operator even in memory during inference.
Per-client storage boundary for raw payloads, intermediate artefacts and outputs, so no tenant data is co-mingled or reachable across engagements.
DORA-grade continuity: regional redundancy, chaos testing, exit strategies for critical third parties.
Hierarchical trace spans for every sub-task, prompt, retrieved document and API call — the reconstructible decision path for Art. 12/14 and PLD disclosure.
Governed runtime workspace operationalizing Art. 10: traceable lineage for every RAG chunk and training record at execution time, canonical version-controlled business glossary (documents Art. 10(2)(d) baseline assumptions), and continuous data-quality monitoring with threshold alerts and logged remediation for the Art. 10(3) 'error-free and complete' standard.
Scoring model: jurisdiction (CLOUD Act exposure), zero-data-retention, BYOK support, audit evidence (C5/AIC4/ISO 42001/EN 18286:2026), tenant isolation.
Authenticated, policy-checked entry point for client payloads; enforces tenant identity, schema validation and rate limits before any data reaches an inference path.