Case Study

How Strivve Uses InfluxDB to Feed Site Artifacts into a Machine Learning System

How Strivve Uses InfluxDB to Feed Site Artifacts into a Machine Learning System

How Strivve Uses InfluxDB to Feed Site Artifacts into a Machine Learning System

Pages 15 Pages

Founded in Seattle, Washington, Strivve (formerly Switch) is the pioneering credit card updater for online accounts, streamlining purchases by centralizing management and updates across tens of thousands of popular websites. Leveraging machine learning and Robotic Process Automation (RPA), Strivve automates payment card placement, digital provisioning, billing/address updates, and card detection—boosting issuer revenue through secure, seamless retention. InfluxDB powered real-time monitoring of automation pipelines, ingesting task success rates, website latency metrics, and transaction volumes at scale. This enabled instant dashboards, predictive failure detection, performance optimization, and global scalability—maximizing card update efficiency and revenue recovery.

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