EU AI Act · Evidence-first governance
The evidence layer for high-risk AI systems — so your teams can explain decisions, review incidents, and prove oversight.
Evidence ingestion
Turn product signals into defensible records — without rebuilding your MLOps or monitoring stack.
Send model metrics, review events, incidents, and system changes through lightweight APIs and webhooks. Guardian ingests what you already measure and links it to an audit-ready governance record.
Live evidence system
Webhook received
Decision event /eu-credit-risk/score
2.4 KB payload
Incoming payload
decision_id: dcs_01842
system: eu-credit-risk
model_version: credit-v3.2
risk_score: 0.81
action: manual_review
timestamp: 2026-05-13T14:09:12Z
Guardian processing
Decision record updated
Scrutiny moment
Decision trace
Can you reconstruct an AI decision made 9 months ago?
Human oversight
Can you prove human oversight actually occurred?
Regulatory scrutiny
Would your current logs survive regulatory scrutiny?
Most AI teams cannot answer these questions months later. Guardian makes the evidence reconstructable, reviewable, and defensible.
Provable scenarios
Three high-risk AI scenarios. One evidence standard: defensible records.
What happenedWhat Guardian capturedWhat regulators verify
Hiring AI
An ML model scored 847 candidates for a logistics role.
Score rationale, feature weights, model version, review notes, and fairness drift signals captured at inference time.
Annex III Article 9 conformity record, reviewer sign-off, and fairness audit trail.
Evidence chain
Decision event
Model version linked
Bias audit attached
Human review assigned
Export-ready record
Credit Scoring
A credit model declined 1,200 applications in Q3.
Decision inputs, threshold state, override log, and model version reconstructed 9 months later.
Full decision audit trail + human oversight record.
Healthcare Triage
An AI triage system prioritized 340 patient cases.
Clinical input snapshot, model version, confidence score, and escalation record locked at decision time.
IVDR-aligned evidence bundle + accountability chain.
Operating loop
Guardian turns scattered AI activity into a structured record your team can review, explain, and export when scrutiny lands.
Production metrics, incidents, threshold breaches, and system changes arrive from existing tools.
Responsible owners are assigned, review states are tracked, and oversight is captured.
Metrics, context, model references, reviewer notes, and events stay linked to one record.
Audit-ready evidence packs can be prepared for internal review, customers, or regulators.
Integrations
Connect Guardian to your existing ML pipeline in minutes.
API, webhooks, monitoring events, model metrics, review signals.
Guardian evidence layer
Audit-readyGuardian evidence layer
Audit-readyReadiness sprint
Guardian helps your team map evidence gaps, assign owners, and create a defensible baseline before expanding across the AI portfolio.
Resources
Practical guidance for teams preparing high-risk AI systems for review, monitoring, and regulatory scrutiny.
Most teams are live in 4 weeks. No infrastructure overhaul required.