How customer data from social media, online forums, complaint platforms, and contact center interactions is unified on a single platform.
How AI uncovers signals that standard quality controls might miss among thousands of customer messages.
How critical findings are shared with quality teams and turned into timely action.
How four critical issues involving vehicles produced between July 2024 and June 2026 were detected early.
How a growing brand portfolio and increasing data volumes are managed without creating additional operational workload.
As one of Türkiye’s leading automotive companies, Tofaş puts every vehicle through hundreds of controls and tests. However, certain issues may only emerge under specific real-world conditions and can remain invisible during standard quality inspections.
In this case study, you will discover how Artiwise CXM helps Tofaş bring customer feedback from multiple channels together, analyze it with AI, and identify critical quality signals before potential issues reach a wider scale.
A real-world example of AI transforming customer feedback from reported data into a proactive, scalable, and sustainable early warning mechanism for quality teams.
Why using the voice of the customer as an early warning source has become critical in modern automotive quality management.
How issues that remain invisible during standard controls can be identified through real-world customer experiences.
Unifying multiple channels, classifying customer feedback with AI, and prioritizing critical quality signals on a single platform.
The end-to-end process from analyzing customer data to sharing critical findings with the relevant quality teams.
The early detection of four critical issues involving vehicles produced between July 2024 and June 2026, supported by a quality management structure built around visibility, scale, and continuity.