Building a Regulatory Risk Copilot with Databricks Agent Bricks (Part 1: Information Extraction)

Summary

This article outlines the process of building a regulatory risk copilot using Databricks' AI tools, specifically focusing on the extraction of structured data from complex PDF documents such as FDA Complete Response Letters (CRLs). It emphasizes the importance of collaborative workflows between AI engineers and business subject matter experts (SMEs) to ensure accurate data extraction and validation. The article details a four-step approach that includes parsing unstructured PDFs, iterative information extraction, evaluation and validation of the extraction agent, and integrating the agent into an ETL pipeline for production use. This unified platform approach aims to enhance the efficiency and accuracy of regulatory data analysis.

Key Learnings

• 1The use of ai_parse_document() allows for efficient extraction of text from complex PDF layouts, significantly reducing the need for extensive coding.
• 2Collaboration between AI engineers and business SMEs is crucial for defining extraction requirements and ensuring the accuracy of the extracted data.
• 3Formal evaluation methods, including ground truth labels and LLM-as-a-Judge, are essential for validating the performance of the extraction agents.
• 4The integration of the extraction logic into ETL pipelines using ai_query() facilitates seamless processing of new documents, enhancing operational efficiency.
• 5Databricks' platform provides a scalable solution for handling large volumes of regulatory documents at a lower cost compared to traditional methods.

Who Should Read This

Senior AI Engineers and Data Scientists focused on building scalable document processing solutions in regulatory environments.

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