Case Study: Clearcover achieves faster model velocity and real-time drift detection with Arize AI

Clearcover Accelerates Model Velocity, Navigates Feature Drift and Drives Confidence In Deployed Models

Clearcover, a tech-driven car insurance company, struggled to scale real-time ML because monitoring via BI tools took >2 weeks per model, introduced ~24-hour detection delays, consumed 9–10 business weeks per year of data science time, and lacked feature-drift monitoring for critical claims and scoring models. To enable real-time production models and faster model velocity, Clearcover selected Arize AI’s ML observability platform after a 2021 proof of concept.

Arize AI delivered end-to-end observability with a central inference store, a Python SDK for instant integration, automated monitors, cohort-level feature and concept drift detection, data integrity checks, and real-time Slack/email alerts. The result: monitoring setup shifted from 2–4 weeks to instantaneous, Clearcover freed >400 hours annually, deployed ~10% more models per year, achieved >150% ROI in the first year with payback under nine months, improved model performance via proactive drift detection, and accelerated time-to-production (e.g., a model into production in 46 days scoring ~50,000 applications daily).

Clearcover

Alex Post

Lead Machine Learning Engineer