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Case Study: Automated PDF Parsing for a Fortune 500 Retail Giant

RPA for streamlining operations for Audit team in an FMCG environment

Overview A Fortune 500 retail company received a large volume of payment-related documents in their email, leading to the need for an automated system to read and extract information from PDF files and store it in a database.

Problem Statement The client required an automated solution capable of downloading, analyzing, and extracting information from a high volume of PDF files, up to 1000 files per day.

Solution Approach We developed an end-to-end solution utilizing open-source technology. First, we set up a system to read emails and download attachments. Next, a PDF parsing module was created to analyze the PDF files and extract the relevant information. The extracted data was then stored in a database, and a dashboard was built to track daily updates.

Technologies Used The entire solution was built using open-source technologies, including Python for coding, Apache NiFi for data flow management, and PostgreSQL for data storage.

Benefits The automated PDF parsing solution saved the client significant time and effort, as they no longer needed to manually analyze the PDF files. With the extracted information stored in a database, the client could easily access and analyze the data to make informed business decisions.

Conclusion By automating the PDF parsing process, we provided a cost-effective and efficient solution for our client. The open-source technology used allowed for flexibility in the system and ease of maintenance. Overall, our solution streamlined the client's payment-related document processing and improved their business operations.

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