Closing the Testing Gap with Synthetic Data

​AI-generated systems can produce production-ready code at speed but without the right data to test against, that speed creates a false sense of readiness.  This session explores how synthetic data bridges the gap between the static datasets used during development and the unpredictable conditions of live environments.

​By generating sample data aligned directly to custom domain models - the actual entities, relationships, and business rules - users can validate what AI-generated systems build and not just what they were designed to do. The result is a closed feedback loop that catches structural gaps early, supports automated testing pipelines, and gives users the confidence in the full generated codebase.