Case Study

7% increased efficiency by using ML to optimize the purchase order process for a Fortune Global 500 chemical company

A Fortune Global 500, one of the largest chemical companies in the world. This $47 billion multinational chemical company employs around 19,000 people and is a global leader in innovation, consistently developing high-quality chemicals, polymers, fuels, and technologies. 


A leading chemical company needed a new way to analyze and audit vendor invoices in order to make better cost-saving decisions in procurement.  

Invoices were saved as images in SAP; purchase order lines were in free-form text.


We developed an ML & ICR-powered solution to extract information from diverse vendor invoice images, built an integrated data model, and provided actionable intelligence to optimize free form text (FFT) PO process.

Tech stack



Savings across undefined spend category.


Workforce efficiency gains.


Model accuracy.

Sustained savings driven by actionable intelligence.

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