{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/6392d554d10b4d66b73f7e76cbdeeb8b\" 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/6392d554d10b4d66b73f7e76cbdeeb8b-fc3e03270d5d5316.gif","duration":152.7585,"title":"Data-Driven Strategies for Agentic AI Workflows 🚀","description":"In this video, I discuss a data-led strategy for utilizing agentic AI and workflows to effectively manage large-scale unstructured data. By applying customizable sentiment and flagging systems, we can automate workflows and enhance the investment process, allowing us to screen thousands of companies more efficiently. I highlight the importance of transparency in data analysis, showcasing how we can drill down into specific metrics and time series information. Additionally, I present a niche use case involving trade tariffs, demonstrating the impact of various changes across companies and countries. I encourage you to explore these customizable approaches in your own workflows to maximize efficiency and insights."}