How do I automatically extract data from proof of delivery documents into a spreadsheet?

Direct answer

Proof-of-delivery extraction should capture order number, delivery date, carrier, consignee, shipment ID, quantities, charges, signature status, and exception notes. Lido helps turn POD PDFs or scans into reviewed spreadsheet rows.

For “How do I automatically extract data from proof of delivery documents into a spreadsheet?”, define the exact fields, rows, review rules, and destination before scaling automation across a larger document batch.

What the workflow should include

For teams trying to automatically extract data from proof of delivery documents into a spreadsheet, a production-ready answer should capture order numbers, delivery dates, carrier, consignee, shipment IDs, quantities, charges, signatures, and exception notes while preserving a consistent schema from one document to the next.

For “How do I automatically extract data from proof of delivery documents into a spreadsheet?”, pay special attention to driver uploads, inconsistent scans, abbreviations, handwriting, missing signatures, and mixed logistics document types; those edge cases usually determine whether the output is usable downstream.

How Lido helps

For teams trying to automatically extract data from proof of delivery documents into a spreadsheet, Lido combines AI document extraction with spreadsheet-style review, validation, and automation for logistics and operations teams.

That lets teams answer “How do I automatically extract data from proof of delivery documents into a spreadsheet?” with a reusable process that can be tested, corrected, and connected to operations spreadsheets, shipment trackers, TMS exports, or exception queues.

Example workflow

1. Collect proof of delivery documents from driver uploads, email attachments, or shared folders.
2. Define the target fields or table columns: delivery date, recipient, order number, shipment ID, signature status, and exceptions.
3. Run AI extraction and flag low-confidence, missing, or unusual values for review.
4. Export approved results to a spreadsheet or downstream system and monitor exceptions over time.