Data Entry Automation with Validation and Reconciliation
Capture data from documents and messages, validate required fields, reconcile records, and route uncertain entries for review.
Extract fields from approved source records, validate them against business rules, and write only reviewed data to the target system.
- Receive the source record
- Extract mapped fields
- Validate and reconcile
AI can prepare and check entries; people remain responsible for ambiguous sources, policy exceptions, and final approval.
What this workflow does
A data-entry workflow should preserve the source for every field, reject uncertain values, prevent duplicate writes, and record any correction.
Use one source-to-system process with a documented schema, validation rules, record owner, and accepted posting state. The workflow can extract and validate fields, while ambiguous records, sensitive data, destructive changes, and final posting remain with authorized operators.
How the workflow runs
Receive the source record
Capture the document or message, source identity, received time, destination system, and expected record type.
Extract mapped fields
Read only the agreed fields and retain the source location for each extracted value.
Validate and reconcile
Check required fields, formats, reference data, duplicates, totals, and cross-record consistency.
Route uncertain entries
Send unreadable, conflicting, duplicate, sensitive, or out-of-policy records to the responsible operator.
Approve and write the record
After required review, write to the intended record and log the values, corrections, reviewer, and result.
Controls to define before launch
| Control area | What the agent handles | What the team controls |
|---|---|---|
| Scope | Allowed data, systems, and actions | Approve source types, field mappings, destination records, validation rules, write permissions, and sensitive-data handling. |
| Review | Approvers and response times | Authorized operators approve ambiguous values, identity or financial data, destructive updates, exceptions, and final posting. |
| Exceptions | Fallback owner and escalation path | Define owners for unreadable sources, duplicates, mismatched totals, missing reference data, and unavailable systems. |
| Evidence | Sources, actions, and corrections | Retain the source location, extracted value, transformation, validation result, correction, reviewer, and final record ID. |
| Recovery | Retry limits and rollback plan | Use stable record keys, staged writes, duplicate protection, retry limits, and a rollback path for failed or partial updates. |
What to do before and after the pilot
Before launch
Start with one stable form or document type, confirm the schema and reference data, and test poor scans, duplicates, missing fields, and mismatches.
After launch
Review field accuracy, manual corrections, duplicate prevention, reconciliation failures, exception handling, write errors, and recovery outcomes.
Connect the workflow with OpenMax
OpenMax can coordinate source access, validation, approval, and controlled system updates with a traceable record of each write.
Frequently asked questions
Where should a pilot begin?
Choose one high-volume record type with a stable schema, clear validation rules, dependable reference data, and an accountable operator.
What must remain under human control?
People retain ambiguous interpretation, sensitive-data decisions, destructive changes, policy exceptions, and final posting approval.
How should teams evaluate the pilot?
Measure field accuracy, correction rate, duplicate prevention, reconciliation failures, exception quality, write errors, and recovery.
