DOCUMENT INTELLIGENCE & AI

LLM-assisted Automatic Feature Extraction for Document Understanding and Analytics

With the exponential growth in the volume and complexity of structured and semi-structured documents, organizations face increasing challenges in extracting meaningful features for downstream analytics. This tutorial introduces a cutting-edge approach to automatic feature extraction using Large Language Models (LLMs), integrating generative AI with document engineering workflows. Participants will explore how LLMs can extract actionable insights from varied document types such as invoices, medical notes, legal filings, and audit reports. We demonstrate the integration of LLMs with machine learning models in end-to-end pipelines involving document parsing, feature extraction, and analytics. Real-world scenarios covered include business intelligence and globally significant domains such as: (1) detecting corruption from audit trails, (2) analyzing pregnancy risks from clinical narratives, (3) uncovering food adulteration in lab reports, (4) investigating accidents through unstructured reports, (5) monitoring war or geopolitical crises via leaked or public documents. This tutorial illustrates how global challenges—often rooted in complex documentation—can be addressed by transforming unstructured data into impactful solutions with social, economic, and ethical relevance.