Case Study: Lyft achieves real-time detection of business risks with Anodot

Ride-share leader uses Anodot to identify business risks in real time

Lyft, a leading ride‑sharing company, relies on its mobile apps and backend systems to match riders and drivers and generate revenue. As the business and its data volumes grew, manually monitoring the exploding number of metrics (including many unique identifiers) became unscalable — undetected anomalies such as failed matches risked lost revenue, customers, and brand reputation.

Lyft implemented Anodot’s AI‑powered time‑series anomaly detection (built on AWS) to automatically learn normal behavior, surface deviations in real time, correlate related incidents, and deliver contextual alerts via email and Slack. The result was faster visibility and root‑cause insight into critical issues, reduced incident resolution time, prevention of revenue leaks, and cost savings from avoiding large in‑house monitoring teams.

Lyft