AI-Driven Metadata Extraction Cuts Processing Time by 80%, Elevates Discoverability and Retrievability

Business Case

The customer is a pioneer in voluntary collective licensing and a trusted leader in copyright, data quality, analytics, and fair data practices. They sought to streamline the extraction and enrichment of bibliographic metadata from thousands of complex PDFs, aiming to make scholarly and standards-based content more discoverable and retrievable across a large digital library. 

The Solution

Impelsys implemented a hybrid AI-led approach with human-in-the-loop to address the customer’s complex metadata needs. Our AI tool, fine-tuned for bibliographic patterns, performed the initial parsing and classification of thousands of PDFs. Automated validation and templating scripts streamlined repetitive tasks and ensured consistency, complementing the AI engine. The team then carried out targeted quality checks and enriched any incomplete records, delivering library-ready metadata with exceptional accuracy and speed. 

Outcomes

The AI-powered metadata extraction reshaped how publishers and research teams access, enrich, and distribute scholarly information. Automated extraction and validation cut manual processing by over 80% and enabled the release of new titles to global digital libraries in record time. The same solution provided instant access to verified, machine-readable studies, reducing research retrieval cycles from days to hours and keeping regulatory submissions on schedule.