Human Validation Loop

Why Human Validation Is Non-Negotiable

AI extraction from unstructured content is powerful but imperfect. A transcript can be misinterpreted. A decision's nuance can be lost in extraction. A workaround can be captured without the critical context that makes it safe to apply. Organisational knowledge is too consequential for successors to act on without the assurance that it has been reviewed by someone who was there.

The Human Validation Loop is the mechanism by which RolegacyAI brings human judgment into the memory creation process — not as a bottleneck, but as a trust-building layer that gives the institutional memory the authority it needs to be genuinely useful.

The Validation Workflow

When the capture and classification pipeline produces a new memory candidate — whether from an AI extraction, a document ingestion, or an automated trigger — it follows a validation workflow:

• Auto-commit: Entries above a high-confidence threshold from high-reliability sources (formally authored documents, directly inputted role holder entries) are committed to the memory store automatically, with a flag indicating they have not yet been explicitly confirmed.
• Role holder review queue: Entries below the high-confidence threshold, or from sources that require verification, are surfaced to the role holder in a review queue. The role holder is shown the extracted entry alongside the source material and asked to confirm, correct, or reject it.
• SME review: Entries in domains where the role holder may not be the appropriate reviewer — for example, technical entries that should be reviewed by an architect, or financial entries that should be reviewed by a finance partner — can be routed to a subject-matter expert reviewer.

Correction and Annotation

Reviewers are not limited to approving or rejecting entries. They can correct the content, add context, adjust the classification, update the confidence rating, and annotate the entry with additional information that was not captured in the original extraction. Corrections feed back into the classification model, improving extraction accuracy over time.

Disagreement Handling

Where a reviewer disagrees with an AI-generated entry but the source material supports the original extraction, the disagreement is recorded as an annotation rather than a deletion. This preserves the original capture alongside the correction, enabling future reviewers to see both the raw extraction and the human judgment applied to it.

Trust Score

Validated entries — those explicitly reviewed and confirmed by a role holder or SME — carry a distinct trust marker in the memory store. This marker is surfaced in retrieval outputs, in successor briefs, and in coverage reporting, so users always know whether the information they are receiving has been humanly validated or is still awaiting review.