A Leading Global Fast-Food Chain Achieves 40% Faster Test Cycles and 60% Effort Savings with Coforge’s AI-Enabled QA Automation

Challenges

• Establishing a QA partner to validate the AI-driven Drive-Thru Order Taker system
• Automating regression test suites for reuse across multiple stores and geographies
• Initiating vehicle arrival/departure and validating HME/EVD/POS signals via AWS, Kubernetes, and APIs
• Capturing greeting messages and processing natural user conversations to place orders
• Validating orders on POS against input menus using API calls
• Handling daypart variations (breakfast, lunch, dinner) in ordering flows

Solution

• Defined a QA process framework aligned to short-, medium-, and long-term business objectives
• Built functional and regression test suites to maximize coverage of application functionalities and user stories
• Automated 90% of the regression suite leveraging Python Robot Framework with a data-driven, keyword-driven approach, using 3 end-to-end scripts
• Designed methods for conversation parsing and response validation
• Embedded daypart switching logic (breakfast, lunch, dinner) into test automation
• Defined an integrated approach for test management tool linkage — automated suite triggers and real-time results updates in Jira-Zephyr

The Impact

• 40% reduction in test cycle time through automation
• ~60% effort savings in test execution versus manual testing
• Regression suite tested with 20+ dialects and multiple languages
• Enabled end-to-end validation of the AI-powered virtual assistant for food ordering
• Accelerated release cycles with quick turnaround time and improved user experience consistency