Semantic Memory at Scale: Engineering Production-Grade AI Systems on Cloudflare

While vector embedding and storage seem straightforward, building production semantic memory demands sophisticated architectural decisions that extend far beyond basic similarity search. This article reveals critical design patterns—data store separation, intelligent chunking, and multi-stage conflict resolution—that are essential for scalable AI memory systems on serverless infrastructure. Understanding these hidden complexities bridges the gap between theoretical AI and real-world engineering constraints.