Background

Frazer Ltd., based in Houston, Texas, is a leading manufacturer specializing in Emergency Medical Services (EMS) vehicles, including mobile clinics, stroke units, and ambulances. Since its founding in 1956, Frazer has been dedicated to innovation in the field of mobile healthcare. With a strong commitment, Frazer has continuously evolved to meet the demands of their clients. To enhance operational efficiency and reduce manual errors, Frazer sought to automate their invoice processing system.

Challenge

Frazer faced significant challenges in their invoice processing workflow. The manual handling of invoices was time-consuming and prone to errors, including invalid invoices, missing purchase orders (POs), mismatched data, and incomplete information. These issues led to inefficiencies and increased the risk of human error, affecting the overall accuracy and speed of financial operations. Additionally, it was estimated that manual document classification could consume up to 2,000 labor hours annually, further highlighting the need for an automated solution.

Solution

Logic Kernel collaborated with Frazer Ltd. to implement an automated invoice processing solution using UiPath and Google AI. The project involved several key steps. First, invoices were gathered from a designated folder in Google Drive. Data was then extracted from an Excel file containing active POs from Syteline. UiPath processed each invoice to determine its validity for vouching by comparing it against the POs. Complex business rules were implemented to categorize and address errors or discrepancies found during processing. Additionally, Document AI from Google was utilized for data extraction, enhancing the accuracy and efficiency of processing invoices from various vendors and formats.

Impact

The automation project led to several key benefits for Frazer Ltd. The integration of UiPath and Document AI significantly decreased the number of errors in invoice processing. The time required to process invoices was greatly reduced, allowing for quicker financial operations. Invoices were systematically organized, ensuring easier access and management. The solution ensured that invoices matched the corresponding POs accurately, reducing the risk of discrepancies.

Conclusion

The collaboration resulted in a successful implementation of an automated invoice processing system. The project not only improved the efficiency and accuracy of financial operations but also showcased Logic Kernel's capabilities in optimizing complex workflows through automation and advanced AI technologies. By streamlining these processes, the solution enhanced operational efficiency, reduced manual labor, and improved overall business performance.

Technology

Some of the technologies and integrations implemented in this project.