We present our submission to SemEval-2026 Task 7, focusing on the MCQ track, where models must identify culturally specific answers across language-region locales. Our system augments a compact open-source model with locale-targeted web retrieval at inference time, requiring no task-specific fine-tuning, and places 10th on the leaderboard. Beyond the submitted system, we explore how retrieval depth and search localization affect performance across locales, finding that localizing search parameters meaningfully shifts the geographic composition of retrieved sources and that gains from retrieval are most pronounced for lower-resource locales. We also investigate whether culturally informed prompt framing can complement retrieval, finding that it does, but only when grounding context is present. Taken together, our results point to inference-time web grounding as a practical path toward more culturally aware NLP under resource constraints.