This paper describes a production minded multilingual system built for the NLP-AI4Health shared task, designed to produce concise, medically accurate summaries and patient friendly answers for Head and Neck Cancer (HNC) and Blood Donation. We finetune Gemma2-2B under a strict model size constraint (<3B parameters) using parameter efficient adaptation (LoRA) and practical engineering to handle long dialogues, code mixing, and multilingual scripts. The pipeline couples careful preprocessing, token aware chunking, and constrained decoding with lightweight retrieval and verification steps. We report per language quantitative metrics and provide an analysis of design choices and operational considerations for real world deployment.