AI Decision Assurance

Stop repeating AI failures.

Notary captures the evidence behind high-risk AI decisions, reproduces failures from recorded conditions, verifies fixes, and turns verified cases into release-gate scenarios.

See how it works Discuss a pilot

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

Illustrative pilot scenario

A regulated lender: prove a decision fix before release.

In an illustrative case, 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 logic routes to underwriting review under the same sealed cassette.

1. CaptureVerification Record for CASE-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 check support release review.

Claim boundary: This is an illustrative customer scenario using demo data. Notary verifies a customer-approved expected outcome under recorded conditions. It does not certify general AI safety, fairness, or regulatory compliance.

How it works

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

1. Capture with the SDK or API

Capture the model input, model or agent output, tool and API responses, policy or configuration version, and the final decision. Evidence is sealed cryptographically into a Verification Record.

Capture only what the decision boundary needs
Runs in your process, offline-capable
Open sealing logic for technical audit

2. Replay & verify the fix

When a decision fails, replay the exact scenario from the sealed cassette and test the proposed fix. Notary reruns the recorded conditions and compares original and corrected outcomes.

Deterministic cassette replay (no production calls)
Mutation testing for fix verification
Sandbox escalation where configured

3. Gate future releases

Verified scenarios become reusable release-gate scenarios. Each release runs a readiness check against the library and returns pass, fail, or system error.

Scenario promotion and scenario runs
Proof of Mitigation and Proof of Readiness
Release Gate pass/fail/error contract

Direction, not current capability: Assisted discovery of candidate scenarios from historical decisions (Scenario Intelligence) is a planned direction. Today capture, replay, fix verification, and release-gate scenarios are driven by the cases you record and label.

Release Gate

A verified case becomes a reusable release-gate scenario. On each release, the readiness check runs the scenario library and returns one of three states. Proof references are shown when a scenario is eligible.

Pass

The recorded scenario reproduces under captured conditions and the expected outcome matches. Release review can proceed with the evidence reference.

Fail

The recorded failure is reproduced and the expected outcome is not met. The gate blocks release until the fix is verified for this scenario.

System error

Replay could not complete (missing or stale evidence, custody, or key-material issue). The gate does not pass on an unknown result.

What the gate is: a deterministic check that a known failure mode is covered and the expected outcome holds for the recorded scenario. It is scenario-scoped and does not certify the safety of the wider system.

Four proofs. One sealed record.

Notary does not just log decisions. It proves four things reviewers need to see, bounded to the tested scenario.

Reproducibility

Replay establishes whether the recorded behavior can be reproduced under captured conditions. It shows the failure is repeatable from the sealed cassette, not a one-off glitch.

Remediation

Does the fix work? By running the proposed fix against the same recorded conditions and verifying the outcome, Notary shows the remediation actually changes this scenario.

Integrity

Was this evidence tampered with? Captured decision evidence is sealed with cryptography and chained, so any alteration is detectable.

Durability

Sealed evidence and proof metadata are designed for durable verification, subject to retained evidence, custody records, and available key material.

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 have solved bias, hallucination, or model drift. Expected outcomes are customer- or reviewer-supplied.

Pilot program

Prove one high-risk AI decision fix before it ships again.

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

A good pilot starts with

One bounded workflow: an AI-assisted decision path you can instrument.
One known failure: a dispute, escalation, override, or error pattern.
One expected outcome: a reviewer-approved behavior for the scenario.
Safe evidence: a demo, test, or appropriately governed capture.

What Notary does

Configure the decision capture boundary.
Produce a sealed Verification Record.
Replay the recorded scenario.
Verify the proposed fix.
Promote the verified case into a release scenario.

What you receive

Captured evidence: decision record and replayable cassette.
Replay & fix results: before-fix reproduction where replayable.
A scoped proof: with stated limitations.
A reusable gate: release-gate scenario for the case.

Out of scope: no broad certification (general AI safety, fairness, or compliance certification); no production mutation (capture-only, sandbox-first); no live production GRC integration promises; no hidden production data access.

Discuss a pilot See the release gate

Typical pilot path: choose the workflow and safe data boundary, instrument capture, replay and verify the fix, then promote to a release-gate scenario. Timing is confirmed during onboarding rather than assumed.

The compliance forensics gap

What reviewers ask

When a high-risk AI decision fails — a loan denial, insurance claim rejection, or medical prior-auth error — the questions are:

Can the failure be reproduced? (Reproducibility)
How do you know the fix works? (Remediation)
Prove this evidence wasn't tampered with. (Integrity)
Can you still prove this later? (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, 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; reviewers have no way to know it wasn't edited after the fact.

Direction

Assisted discovery of candidate scenarios from historical decisions.

Every escalation, override, denial, and complaint your AI agents have produced is a candidate scenario. The planned direction is to cluster historical decisions by intent, outcome, and policy, check replayability, and surface human-labeled failures for 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 reviewers.

Status: automated historical scenario discovery at scale is a planned direction, not a shipped capability. Today you build the scenario library from the cases you record and label.

From captured decision to release gate

See how one AI decision becomes replayable evidence, a verified fix, and a reusable release scenario.

Assurance loop

From configured capture to a reusable release-gate scenario.

Speed: 1x

Capture, not intercepts

Notary records explicitly captured decision evidence. It does not claim to transparently intercept every decision element.

Replay, not evals

Scenarios come from recorded failures, with reviewer-approved expected outcomes, not only synthetic test cases.

Scenario-scoped

Proof is bounded to tested scenarios and customer- or reviewer-approved expected outcomes. Not general safety.

Proof scope: Expected outcome supplied by a reviewer. Capture is explicit and sandbox-first. Fix verified for this scenario. Known failure covered by the release gate. Does not certify general AI safety.

Current vs planned

Capability status reflects what is implemented. Where backend logic exists but UI is incomplete, it is described as available through the platform or API, not as fully operational in every interface.

Available Current product

SDK / API captureExplicit decision-evidence capture.
Verification RecordsSealed, replayable cassettes.
Replayability evaluation
Cassette replayDefault replay method.
Fix verificationMutation-style comparison.
Proof of Mitigation
Scenario promotion and runs
Readiness checks
Proof of Readiness
Release Gate contractPass / fail / system error.

Direction Planned

Automated historical scenario discoveryAt scale, from decision history.
Broad source-system connectors
Production GRC deliveryServiceNow, OneTrust, AuditBoard.
Enterprise inventory
Broader operational trust tooling

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

Defensible denials and claims

NAIC and state insurance AI governance expectations are increasing. Produce defensible evidence for denial and claims decisions before examiner pressure arrives.

Lending & Decisioning

Loan-decision remediation

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

Incident forensics

EU AI Act 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 (Braintrust, Patronus)
Captures decision evidence	✓	✓	—	—
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	—	✓	—

Notary captures selected decision evidence and proves fixes for recorded scenarios. It is designed to feed GRC workflows; native GRC integrations are planned, not claimed as live.