Researchers demonstrate that text embedding models for low-resource languages can be effectively adapted using small-scale synthetic data, even when noisy. The approach challenges the assumption that high-quality, large-scale training corpora are required for performant multilingual NLP. Results suggest meaningful gains are achievable with significantly reduced data overhead.