This client's core policy management system ran on aging on-premise servers with hardware failures becoming monthly events. A previous migration attempt (with a different vendor) had failed catastrophically, causing 3 days of downtime and significant customer churn. The engineering team was understandably risk-averse. Regulators required detailed documentation of data handling during any migration.
We designed a "strangler fig" migration pattern—gradually routing traffic to new services while keeping the legacy system as fallback. We built validation pipelines that ran in parallel for 4 months, comparing outputs between old and new systems. Any discrepancy over 0.01% triggered alerts. We also built rollback capabilities at every stage, which the client insisted on after their previous experience.
The new architecture runs on AWS with multi-AZ redundancy and automated failover. We built a custom ETL pipeline that migrated 15 years of policy data (38M records) with full audit trails for regulatory compliance. Real-time sync kept both systems consistent during the 5-month transition period—longer than the 3 months we planned, because we found data quality issues that required manual review.
Zero customer-facing downtime during migration. We had three internal incidents during the parallel period (two database sync issues, one authentication problem), but all were caught by monitoring before affecting customers. Infrastructure costs decreased 28% despite 2x increase in capacity headroom. System reliability improved from 99.1% to 99.94%. Deploy frequency increased from monthly to twice weekly.
Unified patient data across 12 legacy systems. Reduced duplicate records by 73%—not the 89% we projected, but enough to materially reduce medication errors.
Built an ML-powered routing system that reduced fleet fuel costs by 18%. Took longer than planned because integrating with their dispatch system was harder than scoped.
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