Case Study: a major U.S.-based automobile manufacturer achieves faster, more precise safety issue detection with Progress Semaphore

One American Multinational Corporation harmonizes safety data across 35 countries with Progress Semaphore

One American multinational corporation, a major U.S.-based automobile manufacturer, sought to break down data silos to better understand and prevent vehicle safety defects. Their challenge was harmonizing disparate, unstructured data sources like social media and call center reports, which lacked a consistent vocabulary. To solve this semantic problem, they turned to Progress Software and implemented its Semaphore Semantic AI platform.

Using Progress Semaphore, the company built a sophisticated safety knowledge model to extract and contextualize information from all its data sources. This solution allowed them to precisely and quickly identify safety issues. As a result, Progress Software enabled the manufacturer to proactively make course corrections in its production pipeline, improving vehicle safety outcomes and controlling costs.