Case Study No. 3

AI Agent that transforms scanned packing list into Structured Records

A food company that fulfills approximately 1,700 orders weekly (around 6,800 orders per month) is seeking to build an automated workflow for processing packing lists.

The project involves reading PDF packing lists stored in Google Drive, extracting structured data (order number, product names, and quantities), detecting pen color in checkboxes to assign packer identity (with a predefined mapping), and updating a Google Sheet with the results. The workflow should be automated, low-maintenance once set up, and ideally completed under a $1000 budget.

Industry: Fresh & packaged foods
Order Volume: ≈ 1 700 orders per week ( ≈ 6 800 per month )
Current Assets: PDF packing lists in Google Drive, a shared Google Sheet for fulfilment metrics

The fulfillment team hand-checks every PDF packing list, then re-keys order numbers, SKUs, and quantities into a Google Sheet. With nearly 7 000 orders a month, this manual transcription is slow, error-prone, and offers no clear audit trail of which packer prepared each order.

Project Objective

1. Design and implement a hands-off, low-maintenance workflow that

  • Reads PDFs directly from Google Drive as soon as they land in the folder.

  • Extracts structured data — order number, product name, and quantity — with high accuracy.

  • Identifies the packer automatically by detecting the ink colour used to tick check-boxes (e.g., blue = Alicia, black = Ravi, red = Moana).

2. Writes the parsed data into the master Google Sheet in real time, appending a timestamp and packer name for traceability.

3. All configuration, authentication, and error-handling should run in the background with minimal future upke

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