Scaling test infrastructure for billions of users

Background

A large U.S. technology company operates at global scale, serving billions of users worldwide. At this level, every software release carries significant risk: even a minor defect can impact millions of people instantly. The QA organization was under constant pressure to increase release velocity while maintaining the reliability users expect. Traditional manual regression testing was no longer viable at this scale.

Challenge

Each release cycle required thousands of repetitive regression tests across multiple environments. The process was slow, costly, and heavily dependent on manual effort. Engineers were forced to choose between speed and confidence, leading to delayed rollouts, mounting technical debt, and increased risk of production incidents.

Solution

Cognara deployed a framework of autonomous AI testing agents designed to operate continuously at scale. The system was built to:

• Learn from historical bug data and prior regressions
• Simulate complex, end-to-end user journeys often missed by manual QA
• Execute thousands of test cases in parallel, across environments, without downtime
• Generate structured, real-time reports fully integrated into existing QA workflows

Results

• 60% reduction in testing time, compressing full regression cycles from weeks to days
• Significantly expanded test coverage, including critical edge cases
• Higher engineering leverage, allowing teams to focus on innovation and system design
• Lower operational risk, with fewer emergency rollbacks and hotfixes

Impact Beyond the Numbers

Beyond efficiency gains, Cognara enabled a fundamental shift in how releases were approached. With testing no longer a bottleneck, engineering teams gained confidence to ship faster and experiment more aggressively. Reliability became a built-in capability rather than a constraint, supporting sustained innovation at massive scale.