A Trust Architecture for Enterprise GenAI

The TRACE Stack

How regulated enterprises turn probabilistic AI outputs into governed, auditable decisions.

Trigger·Retrieval·Assessment·Confidence·Escalation

From black box to audit trail

A messy AI completion enters on the left. As it passes through the five TRACE layers, it accumulates the evidence, checks, and accountability that make it defensible. Click any pillar to inspect the layer.

Black-box output

Opaque AI Completion

Recommendation generated, but evidence unclear.

Governed decision

Auditable AI Decision

Evidence-linked, policy-checked, confidence-scored, and human-reviewable.

Evidence attached Policy checked Confidence scored Human owner assigned Audit log created
“Enterprises do not need deterministic models. They need deterministic controls around probabilistic models.”
Interactive simulator

Run a case through TRACE

Pick a sample case or describe your own, then watch the pipeline turn an opaque completion into a governed, auditable decision.

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TRACE in regulated industries

The same architecture, applied across high-stakes domains.

Fintech

SME credit recommendation

Loan recommendation escalated because KYC evidence was incomplete.

Legaltech

Contract risk review

Clause risk linked to playbook source and routed to senior counsel.

Healthcare

Patient summary assistant

AI summary blocked from patient-facing use until clinician sign-off.

Enterprise value

Why governance becomes a competitive advantage, not a tax.

01

Faster risk approval

Evidence-linked outputs clear committees in a fraction of the time.

02

Lower procurement friction

Built-in governance answers security and compliance questionnaires upfront.

03

Higher user trust

Reviewers see sources and confidence, not an opaque verdict.

04

Stronger audit defensibility

Every decision carries a complete, replayable control trail.