IDEXX QueryLab Dx introduces a new approach to expanding access to analytics by transforming natural-language questions into fully deployable analytical workflows within JMP. Rather than limiting AI to query assistance, this system extends the pipeline from intent to execution – generating governed SQL from structured data artifacts and automatically producing JMP Scripting Language (JSL) wrappers for direct deployment to JMP Live.

The system uses a staged, artifact-driven methodology that aligns user intent with enterprise data schemas, ensuring queries are accurate, explainable, and consistent with governance standards. Generated SQL is validated against predefined rules before being translated into JSL, creating a seamless bridge between data access and interactive analytics.

This approach addresses a common organizational challenge: analytics capabilities are often constrained by technical barriers, such as SQL and scripting expertise. By combining natural language interfaces, governed data access, and automated JSL generation, QueryLab Dx enables broader participation in analytics while preserving the rigor required in enterprise environments.

The presentation demonstrates an interactive JMP workflow, showing how users can generate, inspect, and deploy analytical outputs directly from natural-language input. It also highlights design patterns (such as schema-aware query generation, validation layers, and code synthesis), illustrating how AI can move beyond assistance to enable scalable, trustworthy analytic workflows across multidisciplinary teams.