The article outlines a comprehensive framework for evaluating multilingual AI systems' ability to generate accurate Cypher queries from non-English inputs, crucial for global business applications. It presents a detailed evaluation pipeline using Phoenix for observability and DSPy for structured instrumentation, focusing on translating English questions into languages such as Hindi, Tamil, and Telugu, and assessing the quality of generated Cypher queries against English ground truth. The process includes translation quality assessment through back-translation and semantic similarity, cross-lingual Cypher accuracy evaluation using LLM judges, and rich metadata capture for detailed analysis. The framework highlights the challenges of maintaining technical accuracy in multilingual contexts and provides insights into translation quality's impact on downstream tasks, model robustness, and language-specific error patterns. The article emphasizes that translation quality alone does not ensure success in generating correct technical outputs and offers steps for expanding the evaluation to more languages and integrating it into production monitoring systems.