A multinational company was migrating from legacy systems and codebases to distributed systems but struggled to test and evaluate software developments at scale. The order management codebase contained ~2.3 million lines across ~1,750 modules, with ~400 batch jobs supporting operations.

• Minimize impact on production while executing regression and migration tests.
• Reduce effort required for testing across a large, complex codebase and environments.
• Lower risk as new code is released and migrated to distributed platforms.

Designed MAESTRO, a machine‑learning based testing platform that streamlines and automates testing: builds testing playbooks tailored to the codebase, runtime environments, and historical performance. MAESTRO prioritizes scenarios, orchestrates data/setup/teardown, and learns from execution outcomes to improve coverage and efficiency over time.

• Cut testing cycle time from 12 days to ~3 hours.
• Reduced issues/incidents per release from ~17% to ~2%.
• Increased release frequency to slightly more than 3×.
• Lowered MTTR from ~6 hours to ~10 minutes.

Estimated savings: approximately $2.75 million from effort (opportunity + time) alone.

By making testing and evaluation dynamic and adaptive, the company can deliver value‑added services while maintaining an automated baseline for repeatable validation. QA evolves into a blended model that is both business‑ready and QA‑rigorous, improving velocity and confidence across releases.