Real-time risk event discovery is crucial for large-scale cloud-native services, where minutes of downtime can lead to significant financial losses and erosion of user trust. Customer incidents provide a vital signal for identifying risks that monitoring systems may miss, but extracting useful information from this data is difficult due to high levels of noise. Researchers have developed TingIS, a system designed to discover risk events from customer incidents in real-time, even in extremely noisy environments1. By leveraging this technology, enterprises can improve their ability to detect and mitigate technical anomalies, reducing the likelihood of downtime and associated losses. The development of TingIS addresses a significant challenge in the field, where the sheer volume and noise of customer incident data can overwhelm traditional analysis methods. This matters to practitioners because effective real-time risk event discovery can help prevent costly outages and maintain user trust.