AI governance and human review
AI assists recognition/classification; humans authorize high-impact actions.
Versioned profiles
Bind record classes/languages, model/version, confidence thresholds, business rules and QC criteria. Assess inputs before inference and retain original/corrected results and reviewers.
Decisions and exceptions
Review low confidence, metadata conflicts, timeouts and drift against test evidence. Do not apply one accuracy to all records. AI cannot lift holds, authorize destruction or declare legal status.
Training data
No verified policy authorizes training on customer records in this source set. Do not assume that right; any training requires purposes/grounds, DPA, scope and authorization.
Continue the workflow
Two journeys, one records lifecycle
Connect paper and born-digital records through persistent identifiers, policies and evidence.
Read detailsIntegration quickstart
Exercise ingest → job tracking → registration → verification with synthetic data.
Read detailsIntegrity, provenance and evidence
Each evidence type answers a different question; hashes, timestamps and approvals are not interchangeable.
Read detailsStart with a verifiable scope.
Agree record classes, quality criteria, permissions and evidence before scaling.
Plan assessment