video1.0<iframe src="https://www.loom.com/embed/018c20b00b84470da28c89616f870a76" frameborder="0" width="1662" height="1246" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12461662Loomhttps://www.loom.com12461662https://cdn.loom.com/sessions/thumbnails/018c20b00b84470da28c89616f870a76-08ca5df02c2e6377.gif340.148In this demo, I walk through how Adjacent operates using Neo4j for visualization, the Adjacent API for triggering recommendations, and Grafana for monitoring latency and inference over time. I demonstrate the evolution of product relationships starting from a cold state, where querying a product leads to the formation of a local subgraph and the inference of relationships, ultimately improving system performance. Initially, we see a latency of 733 ms drop to around 66 ms as the graph becomes denser and we rely less on vector search. I encourage you to explore the repository for deeper insights into the design and assumptions behind the system. Your feedback and questions are welcome as we continue to refine this approach.