Neural text embeddings allow recommender systems to surface relevant cold-start items - those with few or no interaction history - by computing similarity based on item descriptions rather than behavioral data. When a new item lacks interaction signals, text-based neural embeddings can substitute as a proxy for relevance, enabling the system to recommend it alongside established items without waiting for engagement to accumulate. This is particularly valuable in content-heavy domains such as news or article platforms where new items are published continuously. Teams launching new catalog items can therefore expect meaningful recommendation coverage from day one if rich text descriptions are provided.