A unified platform for designing, executing, and analyzing AI model tests across different providers and modalities.

A modular evaluation framework supporting custom metrics, comparative analysis, and reproducible AI benchmarking.

Quality assurance tooling for AI-driven workflow pipelines — testing each node, validating data flow, and monitoring drift.

Applied AI testing research and methodology development. Bridging the gap between AI testing theory and production practice.

Investigating novel approaches to AI quality: metamorphic testing, adversarial evaluation, and semantic coverage for intelligent systems.

Developing curricula and learning resources for AI testing and quality engineering — from fundamentals to advanced practice.