Data Ingestion from Documents in Life Sciences. 

After almost a year of exploring Generative AI in different use cases, Life Science companies are shifting their focus to provide business value at scale. To get there, AI-ready data is a prerequisite. However, did you know that about 70-80% of your company's data is locked in non-machine-readable documents and thus not ready for Generative AI?

Discover how Acodis transforms complex documents into structured data for Gen AI use cases like Chat / Search (RAG - Retrieval Augmented Generation), Content Authoring, and standardization initiatives.

Key Takeaways: 

• What are the typical challenges companies face working on (Generative) AI projects?  
• Why do I need to structure data for Generative AI?
• Can I extract data from all types of documents?
• How can data ingestion processes be safe, and accurate, but automated at the same time?

Agenda:

• 5 min  -  Welcome and Introduction (Martin Keller, CEO of Acodis)
• 15 min - Key challenges with data companies face,  RAG approach for Generative AI (Florian Follonier, Sr. Partner Solution Architect for Data & AI  at Microsoft)
• 15 min - How to get your data AI-ready, use cases in Life Sciences (Benjamin von Deschwanden, CPO of Acodis) 
• 10 min - Q&A