A leading healthcare organization with operations across Europe & India was looking for a better way to deal with large volume of purchase orders that they received. These purchase orders came in every day through different channels be it emails, PDF’s or scanned documents.
A leading healthcare organization with operations across Europe & India was looking for a better way to deal with large volume of purchase orders that they received. These purchase orders came in every day through different channels be it emails, PDF’s or scanned documents. And every single one of them needed someone to manually open it, read it, key it in, and check it. That process wasn't just slow; it was breaking down under its own weight. What changed, was a middleware solution built around AI-driven document processing. Orders now get picked up automatically the moment they arrive. Key details are pulled from both clean PDFs and messy scanned images. The data gets checked against business rules before it ever touches a database. And when something does need human eyes, there's an interface where teams can step in, fix what's off, and approve it — without wading through inboxes or chasing paper trails.
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Europe & India
Healthcare
The client is a growing healthcare firm with operations spread across the globe. Being focused on vision care products, they received a steady stream of purchase orders from hospitals, distributors, and business partners. This is the kind of volume where even a small error rate compounds quickly.
As the business grew, so did the problem. For a while, the team managed well enough. But more orders meant more emails, more PDFs, more scanned pages. And the team processing them wasn't growing at the same pace. Orders sat longer before someone got to them. Mistakes crept in. Nobody had a clear picture of where things stood at any given moment. DEV IT stepped in to fix it with an AI-powered middleware solution that took the manual steps out of the equation entirely — from extraction through validation through storage.
The process had worked well enough when volumes were manageable. But every purchase order that arrived required manual effort from start to finish. The growing order volume led to a series of operational issues that compounded over time.
Slow Order Processing: Every order required someone to open it, read it, and manually enter the details. That took time and when the queue backed up, fulfilment slipped.
Data Entry Errors: Human input at that scale meant mistakes. It involved quantity errors and wrong pricing. Also, product mismatches were a constant risk.
Limited Visibility: There was no single place to see what was in progress, what was stuck, or what had been completed.
Difficulty Managing High Order Volumes: The process that worked at lower volumes simply didn't scale. More orders meant more hours, not better throughput.
Time-Consuming Validation: Validation took real time. Teams were spending significant chunks of their day just checking order details rather than moving them forward.
Delays in Follow-Ups: When something went wrong — a missing field, a validation failure — there were no automated alerts, so follow-ups were slow and inconsistent.
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Explore more case studyDEV IT designed and deployed a middleware automation solution on Microsoft Azure. It brought together Azure Document Intelligence, Azure SQL Database, SharePoint, and ASP.NET Core APIs. When an order arrives by email, the system picks it up automatically. Azure Document Intelligence handles extraction — pulling the relevant fields from structured PDFs and scanned images without anyone having to open them. Those fields then run through predefined validation rules to catch anything that doesn't add up, and whatever passes gets written to Azure SQL Database and SharePoint, where it's accessible to anyone who needs it.
For orders that need a second look, a .NET interface gives teams a clear view of what was extracted, highlights discrepancies, and lets them make corrections without having to dig back through the original documents. Automated notifications keep things moving, so issues don't quietly stall while everyone waits for someone else to follow up.
The impact was immediate and measurable. Manual processing effort dropped by 95%. Order processing accelerated by 90%. Data accuracy improved significantly, and for the first time, the team had genuine visibility across the entire supply chain workflow, not just whatever happened to be sitting in their inbox.
With this foundation running, the client now processes orders at a pace and accuracy level that simply wasn't achievable before. The manual bottlenecks are gone, the visibility gaps are closed, and the team's attention can go toward work that actually requires human judgment. More importantly, the architecture is already in place to push intelligent automation into other parts of the supply chain — whenever the organization is ready to take that next step.