NLP and data science could be so easy if all of our data came as clean and plain text. But in practice, a lot of it is hidden away in PDFs, Word documents, scans and other formats that have been a nightmare to work with. In this talk, I'll present a new and modular approach for building robust document understanding systems, using state-of-the-art models and the awesome Python ecosystem. I'll show you how you can go from PDFs to structured data and even build fully custom information extraction pipelines for your specific use case.

This talk explores how leveraging Large Language Models (LLMs) to generate structured customer profile summaries improved both compliance analyst workflows and fraud scoring models at a financial institution. Attendees will learn how embeddings derived from LLM-generated narratives outperformed traditional manual feature engineering and raw text embeddings, offering insights into practical applications of NLP in fraud detection.

Graph theory is a well-known concept for algorithms and can be used to orchestrate the building of multi-model pipelines. By translating tasks and dependencies into a Directed Acyclic Graph, we can orchestrate diverse AI models, including NLP, vision, and recommendation capabilities. This tutorial provides a step-by-step approach to designing graph-based AI model pipelines, focusing on clinical use cases from the field.