Coforge helped a global fast-food chain achieve faster test cycles by implementing AI-enabled QA automation for its drive-thru ordering system. The solution combined automated regression, conversational AI, and end-to-end testing to ensure reliability across languages, dialects, and ordering scenarios.
The Challenge
A leading global quick-service restaurant (QSR) brand was rolling out an AI-powered drive-thru order taker system and needed a scalable quality engineering strategy to support it. The initiative demanded faster regression testing, higher automation coverage, and consistent performance across multiple markets, languages, and ordering scenarios.
The company also needed to validate complex interactions spanning vehicle detection, conversational AI, APIs, and POS systems. They chose Coforge to modernize testing and embed automation across the solution lifecycle.
Our Approach
Coforge established a quality engineering framework designed to accelerate testing, increase coverage, and improve confidence in AI-driven ordering experiences. The approach combined intelligent automation, conversational validation, and integrated test management to streamline delivery and support global scale.
Automated Regression at Scale
We automated 90% of the regression suite using Python Robot Framework, creating reusable, data-driven test assets that accelerated execution across stores, markets, and ordering scenarios.
Conversational AI Validation
We developed methods to parse customer conversations and validate AI responses, ensuring accurate order capture and reliable interactions throughout the ordering journey.
End-to-End Drive-Thru Testing
We automated the validation of vehicle events, HME, EVD, POS, APIs, and cloud services to verify performance across the entire drive-thru ecosystem.
Daypart-Aware Automation
Automation logic accounted for breakfast, lunch, and dinner menus, enabling reliable validation of changing ordering flows throughout the day.
Integrated Quality Management
We linked automated testing with Jira and Zephyr, enabling streamlined execution, real-time reporting, and improved visibility across testing and release activities.

Impact to Date
40%
~60%
Business Impact
- Validated regression scenarios across 20+ dialects and multiple languages
- Enabled end-to-end testing of the AI-powered virtual ordering assistant
- Accelerated releases while improving ordering consistency and user experience
With an automation-driven quality engineering foundation in place, the client can expand its AI-driven ordering experience on a global scale while maintaining the speed, consistency, and reliability that customers expect.
