arXiv:2604.21889v2 Announce Type: replace-cross Abstract: Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can result in massive financial losses and diminished user trust. While customer incidents serve as a vital signal for discovering risks missed by monitoring, extracting actionable intelligence from this data remains challenging due to extreme noise, high throughput, and semantic complexity of diverse business lines. In this paper, we present TingIS, an end-to-end system designed for enterprise-gra
The increasing complexity and scale of cloud-native services necessitate advanced real-time risk detection, especially as AI and machine learning capabilities mature to handle noisy, high-throughput data.
Enterprise-grade real-time risk event discovery from customer incidents can prevent massive financial losses and reputational damage by proactively identifying technical anomalies.
The ability to transform unstructured, noisy customer feedback into actionable intelligence for system reliability and security improves significantly, reducing manual effort and response times.
- · Cloud service providers
- · Large enterprises
- · AI/ML solution developers
- · Customer experience platforms
- · Manual incident response teams
- · Legacy monitoring systems
- · Businesses with high downtime tolerance
Improved system uptime and reliability for large cloud-native services.
Increased trust in digital services and potentially lower operating costs due to fewer incidents.
Competitive advantage for companies adopting sophisticated AI-driven incident management, leading to industry consolidation among tech leaders.
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Read at arXiv cs.LG