In the preclinical development phase of drug discovery, timely access to relevant historical data can directly impact decision-making, regulatory timelines and study design. Yet, most pharmaceutical organizations still rely on scattered systems and manual workflows to extract insights from vast volumes of structured and unstructured study data.
In this webinar, we present PRINCE—Bayer’s innovative GenAI-powered research assistant, co-developed with Thoughtworks. Built on a multi-agent architecture, PRINCE combines Retrieval-Augmented Generation (RAG), Text-to-SQL, and LLM-powered reannotation pipelines to bring transparency, speed, and accuracy to data-intensive workflows. It enables scientists to:
- Retrieve high-value study insights across thousands of legacy reports with 90% less manual effort
- Draft regulatory documents in minutes, not weeks
- Re-annotate corrupted or incomplete metadata at scale, improving downstream retrieval quality
We’ll walk through how we productionized the solution and ensured factuality, traceability, and compliance.
Whether you’re exploring GenAI in preclinical drug discovery, compliance-heavy healthcare domains, or enterprise R&D, this session will offer practical patterns for transforming AI prototypes into trusted decision-making copilots.
Speakers
- Dr. Annika Kreuchwig, Senior Principal Data Scientist – Research & Development, Pharmaceuticals, Bayer AG
- Sarang Sanjay Kulkarni, Technical Principal, Thoughtworks
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