From fragmented QA to unified automation

Fragmented QA processes and manual validation methods were limiting speed, scalability, and release confidence. The organization faced several interconnected challenges:

• No unified automation frameworks spanning web, mobile, and AI workflows.

• Absence of structured validation mechanisms for LLM responses.
• High manual effort in validating AI outputs, including web search, RAG, and agent responses.
• Manual validation of AI agents without reusable test components.
• Slow regression cycles caused by duplicated automation efforts.
• No measurable quality metrics for AI-enabled features. 

Nagarro designed a unified automation and AI evaluation framework to support end-to-end quality validation across platforms. The solution included:

• A unified automation framework covering web, mobile, API, and LLM evaluation.

• Integration of Claude Code, Playwright MCP, and Playwright Agent to accelerate script creation and reduce maintenance.
• Structured LLM evaluation using RAGAS metrics, including faithfulness, relevancy, context precision, and context recall.
• Semantic similarity validation with threshold-based automated scoring.
• Reusable test modules for web search, general chat, file attachment, and AI agents.
• CI/CD-ready execution with centralized reporting across platforms. 

The engagement delivered a scalable, data-driven QA ecosystem with that improved speed, consistency, and confidence in AI-driven releases.

• 40% faster automation script development through AI-assisted testing

• 30% reduction in regression execution time
• Quantifiable AI response quality using structured LLM metrics
• A single scalable framework replacing fragmented automation solutions
• Improved confidence in AI-driven releases through data-backed validation