In an age of fast-paced technological advancements, countless companies are intrigued by the potential of GenAI and seek to incorporate it into their operations. Yet, many face uncertainty on where to begin. This case study illuminates how we supported GAEA Global, a prominent provider of supply chain solutions, in pinpointing the optimal GenAI solution tailored to their specific industry requirements.
The goal of GAEA Global was to digitize the variously formatted and currently paper-based audit reports and maintenance logs. These reports were completed by hand and saved for later use. Increasing operational efficiency was one of the many commercial opportunities that came with digitizing this important data. However, creating a traditional system was expensive and time-consuming because of the variety of templates employed.
Reports from GAEA Global were available in a variety of formats, such as scanned documents, handwritten logs, and different digital templates. It was almost impossible to extract structured data using a single digitizing technique.
Scalability was infeasible due to the substantial resources and lengthy development cycles needed to build a specialized system for every distinct template.
Given discrepancies in document structures, it was difficult to extract pertinent information from reports while maintaining high levels of accuracy.
With the overhaul of GAEA Global's document processing system, manual, paper-based reports gave way to a digital workflow powered by AI. We increased operating efficiency, increased accuracy, and simplified data extraction across a variety of report formats by combining machine learning with GenAI.
