Beyond Mountains Of Papers And Datalakes: The Promise Of LLMs In Augmenting Manufacturing

By Yogesh Waghmare, Senior Director, MSAT

Biotech manufacturing has spent years digitizing numerical data, modeling process parameters and quality attributes for predictive insight. But a different category of knowledge, the kind embedded in batch records, SOPs, deviation reports, and change control documents, has remained largely inaccessible. Yogesh Waghmare argues in this presentation that LLMs represent an inflection point for that problem, analogous to fracking unlocking previously unreachable oil reserves.

The case isn't simply that LLMs can read documents. It's that deploying them responsibly in a GMP environment requires a specific framework: AI calibrated to quality procedures, decision rights mapped against error cost and knowledge type, and guard rails that keep the model grounded in source material rather than generating unsupported output. This presentation walks through a Discrepancy Assistant BOT as a concrete illustration of how human-in-loop design works across event classification, impact assessment, investigation, CAPA, and final writeup.

Access the presentation below to learn more.