01 / 05
Enterprise AI Governance

The model proposes.
The harness authorizes.

Razi wraps AI-driven reasoning inside a constraint-governed runtime that enforces schema compliance, policy rules, evidence traceability, and deterministic replay.

Runtime
Deterministic
Model
Proposal Engine
Verification
Mandatory
Policy
Compiled Authority
02 / 05
The Problem

AI without governance
is production debt.

⚡
Silent Hallucinations
Model outputs that pass basic checks but contain fabricated evidence, unsupported severity classifications, or invented justifications.
↻
Non-Reproducible Failures
When an AI decision goes wrong in production, there is no trace, no replay, no way to reconstruct what happened or why.
📝
Policy Violations
Internal notes leaked into customer-facing output. Severity downgrades violating SLA contracts. No structural enforcement exists.
🔧
Rebuilt Every Time
Every team hand-builds schema validation, retry loops, guardrails, and logging. No standard. No reuse. No audit trail.
# Today's pattern
response = openai.chat(prompt)
output = json.loads(response) # fingers crossed
send_to_customer(output) # no policy check
# no trace. no replay. no audit.
03 / 05
Interactive Execution — Mirrors Real Recorded Runs

> razi run

Every scenario below is a real spec you can run yourself: specs & inputs · recorded runs & replay reports

Pick a workflow — any industry, one harness
evidence.index
Synthesis Strategy max_attempts: 3
Try breaking it — make the model misbehave (pick one)
04 / 05
Deterministic Replay

razi replay

escalation_qualification__20260303T152324Z__b2cc8332

Without calling the model. Six months from now. In an audit. The harness re-runs validation and policy evaluation against stored artifacts and proves determinism.

replay_report.json
✓
schema_matchtrue
✓
policy_matchtrue
✓
template_hash_matchtrue
✓
final_output_matchtrue
✓
mismatches[]
PASS
20260303T153051Z — determinism confirmed
final_output.jsonAUTHORITATIVE
policy_compliant + violations set by runtime authority, not model
Run Artifacts
runs/escalation_qualification__20260303.../
├─ evidence_index.json
├─ attempts/attempt_1,2,3/
├─ final_output.json
├─ trace.jsonl
└─ replay_report.json ✓
05 / 05
Why Razi

Governance baked into
compilation. Not convention.

01
Policy is Compiled
Rules are not prompts or team conventions. They are compiled artifacts that the runtime enforces deterministically on every run.
02
Replay is Structural
Every run produces a complete artifact set. Any decision can be replayed without calling the model, months later, in an audit.
03
Evidence is Enforced
Claims without evidence IDs are rejected. Hallucinated IDs are caught. The harness, not the model, is the authority.
Ready to govern your AI workflows?
Razi — Escalation Qualification. Early design partners welcome.
Get Early Access Star on GitHub