video1.0<iframe src="https://www.loom.com/embed/1d0925a7d75342cb8b1d78569ecf9723" frameborder="0" width="1500" height="1125" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>11251500Loomhttps://www.loom.com11251500https://cdn.loom.com/sessions/thumbnails/1d0925a7d75342cb8b1d78569ecf9723-d6af62b21bb11af0.gif503.18This is SoundFinder 9001, my Applied AI Systems final project, extending the Module 3 Music Recommender. I added an AI-assisted Profile Builder, a RAG grounded explanation using a knowledge base, and a Reliability Harness that runs the full pipeline. I built a study profile for rock acoustic guitar, focus mood, and generated five recommendations with score breakdowns and a top pick like Drift Beacons. The harness ran in about 5 minutes, used five fresh personas, made about 20 API calls per run, and cached results. No action is requested from you, but you can try different profiles and song counts.