A human review gate connects each internal deliverable to the evidence and reviewer authority required before the workflow may continue.
Classify the deliverable before review
Define the internal deliverable class, intended user, allowed use, known error modes, evidence sources, reviewer role, prohibited decisions, and consequence of an incorrect release. Define the unit of work, the people and systems involved, the evidence already available, and the exact decision this record must support. A narrow boundary keeps the analysis tied to an observable process instead of turning it into an open-ended inventory.
NIST's AI Risk Management Framework organizes AI risk work around governance, mapping, measurement, and management rather than treating review as a single approval click. Preserve the source URL, version, retrieval date, and relevant rule beside the local implementation decision. If the source does not address the buyer's environment directly, label the local conclusion as an adaptation and retain the assumption that connects them.
Show evidence and required assertions
The review record should join output revision, claims, source links, deterministic checks, risk tier, reviewer, questions, hold reason, revision request, escalation, and final disposition. Each record needs a stable identifier, owner, current state, source reference, last verified time, exception path, and next permitted action. Conflicting or missing evidence remains visible so a later reviewer can distinguish a confirmed result from inference, recollection, or an unavailable signal.
Each risk tier needs a named reviewer, required evidence, acceptance assertions, mandatory holds, escalation owner, and final authority that automation cannot bypass. Write the decision rule before automating it, including who may approve, what evidence is required, which condition causes a hold, and how an exception expires. This makes the control testable and prevents a tool from quietly expanding its own authority.
Test hold, revision, and acceptance
Route supported, unsupported, conflicting, stale, missing-source, wrong-reviewer, prohibited-use, revised, rejected, and accepted cases through the staging workflow. Record the fixture, versions, environment, expected result, actual result, reviewer, and corrective action for every failed case. Rerun the accepted cases after a source, permission, workflow, or dependency changes so an old passing result is not presented as current evidence.
Human Review and Acceptance Control System is operated by Reality Contact, LLC. The buyer excludes consequential decisions and retains every final judgment; Reality Contact, LLC implements only the accepted internal review and evidence workflow. The resulting guide and implementation evidence cover only the named sources, workflow, versions, and acceptance cases, so the buyer retains authority over policy, credentials, production use, and later changes.
Where the service stops
Reality Contact, LLC implements bounded review controls but does not make regulated or high-impact decisions, replace accountable reviewers, verify every source, provide legal advice, approve production, or operate review indefinitely. The buyer excludes prohibited decisions, appoints accountable reviewers, confirms evidence and risk tiers, retains every final decision, and approves which internal deliverables may enter production. This is technical workflow implementation and document preparation; it does not replace professional legal, compliance, privacy, security, editorial, or domain review. The system does not promise factual correctness, unbiased judgment, complete source coverage, or safe use outside the accepted internal deliverables and calibration cases.
Sources: NIST AI Risk Management Framework; LangSmith evaluation documentation.