Abstract:
This joint session, presented by Chris Dayton (QualityAssured.AI) and Felipe Fontanet (GMP Bridge), integrates architectural best practices with real-world QA and data governance foundations for compliant AI deployment in the life-science industry.
The first portion, led by Chris Dayton, focuses on the architectural decisions that determine whether an AI system can be trusted in regulated environments. Topics include on-prem versus cloud deployment, static versus continuously updated models, lifecycle control, version locking, reproducibility mechanisms, traceability, and the architectural patterns most aligned with emerging expectations in Europe. This section provides a clear and practical framework accessible to both technical and non-technical participants.
The second portion, led by Felipe Fontanet, examines the data foundations required for compliant AI, emphasizing how gold-standard data must be curated, governed, and approved by QA to ensure defensibility. Using examples from EU GMP operations, including deviation narratives, hybrid batch records, environmental monitoring data, and CAPA evidence, this section highlights how data inconsistencies, lineage gaps, and siloed systems often pose greater regulatory risk than the AI model itself.
Together, the session delivers a practical blueprint for designing AI systems where architecture and data handling work together to meet European regulatory expectations.
What attendees will gain from this session:
Attendees will understand the architectural choices that shape AI behavior and regulatory defensibility, including how versioning, reproducibility, and deployment models influence traceability and audit readiness.
They will also gain a practical understanding of data readiness requirements, including how QA defines gold-standard data, how structured and unstructured data must be curated, and how real GMP evidence such as deviations, environmental monitoring, CAPA, and batch records determines whether AI outputs can be defended during inspections.
How attendees will stay engaged:
Clear diagrams, architectural comparisons, and real GMP examples make the material intuitive and immediately applicable. Side-by-side frameworks help attendees understand trade-offs in architecture and data strategy. Short scenarios encourage participants to map concepts to their own environments. The session closes with a Q and A segment to discuss typical challenges and practical considerations for compliant AI adoption.