Publisher currently receives orders from its customers by email, and customer service agents process emails (including attachments), and prepares orders that are punched manually into ERP order management system for subsequent fulfilment and payments.

Orders arrive by email as files a machine can read, as scans, as handwriting, and sometimes as a few lines in the message itself. An agent opens each one and retypes title, ISBN, quantity, address and due date into the ERP system.

Handling high volumes of orders in various formats result in significant inefficiencies, errors, and delays. Various formats in which orders come in include machine readable, scanned and hand- written documents as email attachments. Sometimes order details come in the body of the email itself. Each order document could have multiple orders to be shipped to different locations at different point in time.

Standardizing the extraction of key order details—such as book title, ISBN, author, quantity, shipment address, and due date, from various formats of email orders, without manual effort is the solution customer was looking for.

The implemented solution features a centralized order collection system that automatically gathers order data from multiple channels. An intelligent automation pipeline processes incoming emails with/without attachments, extracting critical details such as client names, addresses, book titles, ISBN numbers, and order quantities. The validated information is then efficiently processed and stored within the backend system, ensuring a smooth and reliable workflow that significantly reduces manual effort and reduces the possibility of errors.

Solution includes human validation of extracted data and correct if any missing or incorrect data. It also tracks audit of all manual intervention's.

GEN-AI powered Intelligent Document Processing, advanced data extraction technologies, and modern workflow automation solutions.

The automation of the order processing workflow has led to a 75% boost in productivity by eliminating manual intervention and ensuring data accuracy. The solution not only minimizes errors but also provides robust internal tracking for enhanced performance monitoring and resource optimization.