Modern retail demands personalized product discovery and natural conversational assistance. Target established the Guest Product Confidence platform to build trust and guide purchasing decisions. An example is the Gift Finder chat agent, launched during the 2025 holiday season, which offers conversational item discovery. To achieve real-time personalization and semantic responses at scale, Target needed to unify its fragmented data ecosystem into a single platform. This platform had to support transactional workloads, graph relationships, vector similarity search, and keyword search concurrently. Target chose Spanner Graph to build its enterprise ontology, a "graph-of-graphs" for a generative AI-powered shopping graph. By unifying data, Spanner now acts as the single source of truth for transactional state and semantic intelligence. The architecture includes enterprise augmentation, a unified graph/vector/search store, and an orchestration layer powering AI interfaces. A zero-downtime migration in four phases, including schema mapping and parallel data replay, was successfully executed. This consolidation led to measurable business impacts, including improved recommendation relevancy and enhanced guest satisfaction. It also resulted in a 50% reduction in infrastructure maintenance, accelerating the development of new AI features.