AI Decision Assurance with Scenario Intelligence

Stop repeating AI failures.

Notary turns high-risk AI decisions into sealed, replayable scenarios. In the Harborline demo, a personal-loan denial is replayed from captured evidence, blocked before the fix, verified after the fix, and promoted into a release gate.

See Harborline path Request pilot access

Works alongside your existing AI, observability, support, and compliance systems. Proof is bounded to tested scenarios and customer-approved expected outcomes.

Flagship pilot story

Harborline Credit Union: prove the loan-denial fix before release.

A thin-file personal-loan applicant was denied when missing or borderline bureau evidence should have routed to underwriting review. Notary captures the decision evidence, replays the exact scenario, verifies the corrected behavior, and turns the failure into a release gate.

Blocked gate
Before fix

Expected UNDERWRITING_REVIEW, got DENY. Release should not proceed.

Passing gate
After fix

Fixed agent routes to underwriting review under the same sealed cassette.

1. CaptureVerification Record for HLCU-PL-0427, sealed with recorded decision evidence.
2. ReplayOriginal DENY is reproduced from the cassette without production calls.
3. Verify FixMutation test proves the corrected outcome for this scenario.
4. Gate ReleaseScenario result, evidence refs, and readiness certificate support release review.
Claim boundary: Harborline is a demo scenario. Notary verifies the fix for this recorded personal-loan scenario under captured conditions. It does not certify general AI safety, guarantee fairness across all applicants, or claim active production GRC integrations.
Design-partner pilot

Turn one known AI decision failure into release-gate evidence.

The pilot is intentionally narrow: one regulated decision workflow, one known failure or dispute pattern, one reviewer-approved expected outcome, and one replayable scenario that can block a bad release.

Best-fit teams

  • Industry: lending, insurance claims, prior auth, or other regulated decisioning.
  • Workflow: repeated AI-assisted denials, escalations, overrides, or customer disputes.
  • Owner: product, compliance, QA, or model-risk lead who can approve expected outcomes.
  • Boundary: sandbox or non-production demo data first; no real customer data required for initial evaluation.

What you provide

  • Decision flow: the agent path or workflow to instrument.
  • Known pattern: one failure mode, dispute, override, or policy gap.
  • Expected outcome: reviewer-approved behavior for the scenario.
  • Release context: where the scenario should inform release review.

What Notary returns

  • Sealed evidence: captured decision record and replayable cassette.
  • Replay result: before-fix reproduction of the failure where replayable.
  • Fix proof: scoped proof that the proposed fix changes this recorded scenario.
  • Release gate: scenario result and evidence refs for release review.

What stays out of scope

  • No broad certification: not general AI safety, fairness, or compliance certification.
  • No production mutation: demo starts capture-only and sandbox-first.
  • No GRC promise: exports/integrations are planned, not claimed as live production integrations.
  • No hidden data access: credentials and production data require a separate security decision.
Week 1

Choose workflow, failure pattern, expected outcome, and safe data boundary.

Week 2

Instrument capture and produce the first sealed Verification Record.

Week 3

Replay, verify the fix, and issue scenario-scoped proof.

Week 4

Promote to scenario and run release-gate rehearsal with evidence refs.

Apply for design-partner pilot Review Harborline example

The compliance forensics gap

What regulators demand

When a high-risk AI decision fails — a loan denial, insurance claim rejection, or medical prior auth error — regulators and courts ask four questions:

  • Why did this decision happen? (Causation)
  • How do you know the fix works? (Remediation)
  • Prove this evidence wasn't tampered with. (Integrity)
  • Can you prove this five years from now? (Durability)

What observability tools provide

Tools like Datadog, LangSmith, and Langfuse show you what happened: log events, model calls, API responses.

They don't show why it happened. A log timeline shows correlation, not causation. The agent's logic could have failed, or the API response could have been stale, or the cache could have been wrong.

They can't prove fixes work. You deploy a fix and hope. Verification requires re-running the exact scenario and checking the outcome — something monitoring systems don't do.

They have no tamper evidence. A log file is just text; regulators have no way to know it wasn't edited after the fact.

Scenario Intelligence

Your decision history is a scenario mine, not a log.

Every escalation, override, denial, and complaint your AI agents have produced is a candidate scenario. Notary clusters historical decisions by intent, outcome, policy, and override pattern, checks replayability, and promotes human-labeled failures into your release gate.

Novel scenario discovery

Surface recurring failure patterns that no one has manually reviewed. Find the unknown unknowns in your decision history.

Policy gap analysis

Compare stated policy against actual AI outcomes and human overrides. Where is the agent diverging from intent?

Regulatory scenario mapping

Map candidate scenarios to NAIC, HIPAA, FCRA, GLBA, ADA, and EU AI Act obligations. Know which historical failures matter to regulators.

Managed library expansion

Notary surfaces candidates monthly. Your team labels the expected outcome. Your regression suite compounds with real production failures.

The compounding loop: One failure becomes one scenario. Each scenario can gate future releases. Over time, your release review hardens against known failure modes. This is where Notary turns compliance evidence into competitive advantage — fewer regressions, faster shipping, and defensible releases.

For design partners

Bring one high-risk decision workflow, one known failure or dispute pattern, and a reviewer who can approve expected outcomes. Notary helps turn it into a replayable scenario and scoped release-gate evidence.

For investors

The wedge is AI decision assurance for regulated workflows: capture evidence, prove scenario-specific remediation, and compound a library of release-gate scenarios from real failures.

See Scenario Intelligence in motion
Watch how historical AI support failures become labeled, replayable, release-gate scenarios.

Scenario Intelligence Demo

How real overrides become replayable release-gate scenarios.

Speed: 1x

Not just evidence

Notary finds recurring failure patterns in historical overrides and escalations.

Not just evals

Scenarios come from real production failures, not only synthetic test cases.

Not general safety

Proof is bounded to tested scenarios and customer-approved expected outcomes.

Proof scope: Expected outcome supplied by QA lead. Production is capture-only. Fix verified for this scenario. Known failure covered by release gate. Does not certify general AI safety.

Four proofs. One sealed record.

Notary doesn't just log decisions. It proves four things regulators need to see.

🔍 Causation
Why did this decision happen? By replaying the exact recorded scenario, we prove the agent's logic — not a transient glitch or stale data — caused the failure.
✅ Remediation
Does the fix work? By running the proposed fix against the same recorded conditions and verifying the outcome, we prove the remediation actually solves the problem.
🔒 Integrity
Was this evidence tampered with? Captured decision evidence is sealed with cryptography and chained into a Merkle tree. Any alteration is immediately detectable.
⏰ Durability
Can you still prove this five years from now? Yes. The cryptographic proof remains verifiable from sealed evidence and included proof metadata over time.
What Notary does NOT claim: These proofs are bounded to the tested scenario. Notary verifies that a fix produces the expected outcome under recorded conditions. It does not certify that your AI system is safe in general, or that you've solved bias, hallucination, or model drift. Use Notary alongside your existing safety and monitoring tools.
Proof scope: Notary verifies a customer-approved expected outcome under recorded scenario conditions. It does not certify general AI safety.

Built for high-stakes AI decisions

Notary's wedge is proving fixes work. The sharpest pain is in regulated industries where AI failures have financial and legal consequences.

🏥
Insurance & Claims
NAIC and state insurance AI governance expectations are increasing. Produce defensible evidence for denial and claims decisions before examiner pressure arrives.
💳
Lending & Decisioning
OCC and Fed model-risk expectations reward documented AI defect remediation. Show that loan-denial failures were caught, fixed for recorded scenarios, and added to release review.
⚖️
Compliance & Legal
EU AI Act (Article 10) and SEC disclosure rules increase incident-forensics expectations. Notary produces scenario evidence designed to support GRC workflows as integrations come online.

How Notary is different

Notary Observability (Datadog, LangSmith) GRC Tools (OneTrust, ServiceNow) Eval Tools (LangSmith, Braintrust, Patronus)
Captures production failures ✓ ✓ — —
Replays failures deterministically ✓ (core) — — —
Verifies fixes work ✓ (core) — — —
Cryptographic proof (tamper-evident) ✓ (core) — — —
Gates releases against known failures ✓ (Scenario Library / release review) — — ✓
Integrates with GRC systems Planned / designed for ServiceNow, OneTrust, AuditBoard — ✓ (native) —
Compliance reporting Evidence mapped to EU AI Act, NAIC, SEC workflows — ✓ —

How it fits your stack

Notary sits between observability, governance, and GRC. You keep your existing monitoring tools (Datadog, LangSmith). Notary captures selected decision evidence, proves fixes work for recorded scenarios, and produces defensible artifacts designed for GRC workflows.

Step 1: Capture with the SDK

Add Notary's lightweight Python SDK to your AI agent. It records explicitly captured decision evidence, seals it cryptographically, and produces a sealed cassette for Notary verification.

  • Runs in your process, zero external dependency
  • Works fully offline
  • Open-source sealing logic for technical audit

Step 2: Replay & verify

When a decision fails, use Notary to replay the exact scenario and test fixes. Notary runs your fix against the recorded conditions and proves or disproves remediation.

  • Deterministic replay from sealed cassette
  • Mutation testing for fix verification
  • Sandbox escalation where configured

Step 3: Gate future releases

Verified scenarios become permanent regression tests. Every release is flagged for manual review against the Scenario Library. Automated CI/CD gating is next.

  • Scenario Intelligence mines your history
  • Manual release review against scenarios
  • Automated CI/CD gating (planned)
  • Proof of Readiness certificates (planned)

Built for defensibility

Cryptography you can trust

  • HMAC-SHA256 sealing on captured decision evidence
  • Merkle chaining for tamper-detection
  • Asymmetric notarization (ECDSA/RSA)
  • Bring-your-own-key option (managed custody)
  • Open-source SDK sealing path for technical audit

Regulatory coverage

EU AI Act (Article 10) NIST AI RMF SEC AI Disclosure OCC Model Risk HIPAA FCRA NAIC

Notary generates evidence that maps to compliance requirements. Evidence exports for ServiceNow, OneTrust, and AuditBoard are planned as enterprise integrations come online.