The source stays in sight
Inspect a document image beside extracted names, identifiers, and dates. Review the exact source before accepting a value, with image adjustments available when a scan needs attention.
Read the document. Trust the review.
Extraction is a starting point. Bring the source and its suggested fields together, then let the reviewer decide what belongs in the record.
Apply the fields you choose
Keep accepted and rejected suggestions separate. Select individual values to apply rather than replacing a complete record simply because an extraction finished.
A recoverable workflow
Uncertain results and service errors deserve clear next steps. Preserve the user’s metadata, review the source, retry when appropriate, or enter the correct value manually.
How the work moves
Start with the sample and follow the path through the actual workspace.
- Open the intake scenario with a supplied sample document.
- Inspect the sample image and prerecorded extraction.
- Choose the suggested fields to apply and check their confidence.
- Save the local record and inspect the updated document health.
Before you explore
Does the public demo send files to an OCR provider?
No. The sandbox uses supplied sample results, and selected local files stay in browser memory.
Can a reviewer correct the extracted information?
Yes. Reviewers can choose fields, reject suggestions, and correct metadata before saving.
Does OCR remove the need for human verification?
No. Names, document numbers, and dates should be checked against the source before they become operational data.
Make it work for your team.
Tell me how your documents move today. We’ll discuss the installation, the workflow, and what needs to fit.
Hasanain.a.mohsin@gmail.com
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