Migrating legacy documentation often reveals a critical bottleneck for enterprise operations: trapped, unstructured data. Red Hat’s Procurement team recently faced this challenge when migrating over 32,000 legacy vendor contracts to a new Contract Lifecycle Manager (CLM). The new system required specific metadata - such as creation dates, expiration dates, and signatories - extracted from a complex mix of generated and scanned PDFs. These documents spanned multiple languages and featured non-standard layouts and tabular data. Completing this extraction manually was projected to take over a year, consuming approximately 14,000 hours and $700,000 in operational costs.

This session details how Red Hat engineered a reusable, AI-driven automation pipeline to solve this challenge in a fraction of the time. Hosted on Red Hat OpenShift AI, the solution utilizes Docling to convert highly unstructured PDFs into Markdown. From there, the text is processed through a Qwen2.5 32B LLM to intelligently extract and format the required metadata into structured JSON.

By transitioning to an automated, unstructured-to-structured ETL approach, the team completed the migration in just 3 months. The project required only 1,700 hours of development and review, ultimately saving 75% in FTE hours and achieving $600,000 in cost savings.