Large online platforms face the challenge of organizing billions of items into navigable shopping collections. Historically, these collections relied on user search history and manual curation. However, multimodal large language models (LLMs) now enable generating collections directly from content, while still considering user search patterns. This paper introduces Pinlanding, a production pipeline for shopping collection generation. Pinlanding comprises four components: understanding user search intent, building a shopping collection vocabulary using LLMs, constructing feeds from attributes, and evaluating/evolving the system. User interaction data helps characterize shopping intents, revealing both high-volume searches and emerging long-tail conversational queries. A vision-language model generates initial product attributes, which are then curated into a compact vocabulary using statistical filtering, embedding-based clustering, and LLM-assisted review. A CLIP-style dual-encoder model is trained for scalable attribute assignment, efficiently mapping products to attributes. Ray is used for scalable batch inference in attribute assignment, and Spark constructs feeds by scoring product-topic relevance. The CLIP-based classifier shows superior performance on a fashion attribute prediction benchmark. Human evaluation demonstrates that Pinlanding significantly improves precision in collection quality compared to traditional methods. The system has led to a four-fold increase in unique shopping topics and a 35% improvement in search performance. Future work involves integrating social trends and developing an AI-agent layer to handle emergent composite concepts.