{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/d8142b6a714646dcb6d1992f50972621\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/d8142b6a714646dcb6d1992f50972621-f54ec073d94cdd83.gif","duration":400.768,"title":"Building an AI-Native Account Prioritization Engine in Clay","description":"A walkthrough of the Clay workflow built during AlphaForge’s GTM Engineering Bootcamp. This demo shows how the operating model described in Building an AI-Native Account Prioritization Engine was implemented—from data enrichment and qualification through prioritization, segmentation, and outbound recommendations. It serves as the working artifact behind the accompanying case study."}