Objectives

• Simplify the manual process, enable analysis, and classification of such documents through digitization and extract the data they contain
• Work across documents of different sizes, layouts, formats at scale
• Integrate the extracted information with backend platforms on real-time basis with relevant audit logs
• Ensure accurate transaction per minute processing performance to handle tens of thousands of documents

Solution

We built a custom solution that could extract data from documents of various sizes, layouts and formats at scale. 

• Developed an independent microservice to read email and fetch attached documents at scale 
• Leveraged multiple technologies including Python, Adobe, Pandas, Numpy and AWS Textract to address complexity and improve readability of documents and data 
• Built run-time intelligence to minimize usage of high-cost components and make it cost-effective

Outcomes

The custom-built solutions helped the client meet all processing SLAs with business-ready information on a day-to-day basis resulting in significant reconciliation accuracy as well as huge cost and time savings; with negligible human intervention thereby reducing human errors and improving overall performance of the company