These cookies are required for the website to function properly. They ensure security, enable basic features like page navigation, and store user session data. You cannot disable these cookies.
- Data Entry & Validation
- Frequently asked questions
Questions, answered
Answers for teams preparing a controlled import process.
Data Entry & Validation
9 questions
Can't find it? Write to support@erp.ai.
How do I get started?
Click Get started, then use Proto to configure Data Entry & Validation for your business. Begin with one organization, one validation definition and a sample batch that includes a rejected row.
What identifies an import?
A batch has a number, owner, organization and definition. Its source hash and revision help the team distinguish a corrected submission from an earlier version.
How are errors reviewed?
Open rejected row results and check the source record identifier, failed field, rule, severity and failure reason before correcting the source.
Who approves a batch?
The Independent Reviewer records a decision for the exact source revision, including a reason when the batch is returned or rejected.
What happens after approval?
The Execution Operator handles the accepted partition and retains run, step, attempt and receipt evidence against the target reference.
How do we avoid a duplicate commit?
Check an unknown attempt against its operation key, provider reference and receipt before another attempt is made.
Can we correct a completed import?
Use a successor revision and a follow up that keeps the earlier decision, run and receipt visible for verification.
Where do source documents go?
Attach them to evidence documents for the related batch and record who verified them.
What can we report on?
Review batch volume, validation outcomes, reviewer decisions, execution states, follow up work and transition history from the corresponding records.
Set up Data Entry & Validation your way.
Start free, then use Proto to change fields, steps and approvals before your team goes live.