Ontology-backed semantic layers The foundation for production-ready AI

Many AI projects across domains deliver convincing early results but can stall before reaching production. If your team is navigating that gap, this webinar will be of interest.

The problem is rarely the AI itself. Rather, it's the semantic foundation underneath it. Without a governed layer of shared scientific meaning or enterprise knowledge, AI systems may struggle to consistently interpret terminology, connect related concepts, or explain how they reached an answer. That makes outputs hard to trust and harder to act on in a scientific context.

This webinar explains what semantic layers are, how they can address these gaps, and what it takes in practice to build and maintain them in pharma and other R&D environments.

You'll walk away understanding:

• Why data quality alone doesn't guarantee reliable AI outputs, and where semantic gaps typically appear
• How ontology-backed semantic layers support entity recognition, data normalization, and explainable AI retrieval
• What it takes to build and evolve ontologies efficiently, with the right domain experts involved
• How semantic infrastructure can underpin trustworthy, auditable agentic workflows in scientific settings