"Decoding Data and Driving Action: NLP, LLMs, and Agentic Recommenders"

Abstract: How do we bridge the gap between raw data and automated business strategy? This session provides a technical deep dive into four distinct implementations of AI designed to solve this problem across the healthcare, insurance, and retail sectors. We will begin by detailing the specific NLP techniques required to identify medicines and pathologies within unstructured clinical narratives, followed by a look at how Generative AI is transforming information extraction in the insurance domain. The focus then shifts to the architecture of personalization systems, comparing Deep Learning approaches for product affinity against tree-based models for content prioritization. Finally, we will define the emerging role of the "Agentic Orchestrator," a system that synthesizes these predictive insights to autonomously decide the optimal marketing engagement for every customer interaction.

Bio: With a Ph.D. in Artificial Intelligence from the University of Verona, Riccardo Sartea bridges the gap between theoretical foundations and practical application. Currently a Data Scientist at Amazon Web Services Zurich, he focuses on architecting end-to-end machine learning solutions that scale from initial concept to production. His recent work centers heavily on the lifecycle of Large Language Models, applying techniques like fine-tuning and knowledge distillation to address diverse use cases. Beyond the algorithms, he collaborates closely with engineering and product teams to translate ambiguous challenges into concrete, data-driven technical strategies.