video1.0<iframe src="https://www.loom.com/embed/6392d554d10b4d66b73f7e76cbdeeb8b" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/6392d554d10b4d66b73f7e76cbdeeb8b-fc3e03270d5d5316.gif152.7585In 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.