Building the digital foundation for an emerging AI certification standard.

AIQCI is an early-stage initiative focused on defining and validating professional standards for AI quality control and governance. It combines open education, structured evaluation, and certification into a unified system that makes AI reliability measurable and independently verifiable.

As AI systems move into production, organizations lack consistent methods for evaluating performance, managing risk, and demonstrating reliability. Existing approaches are fragmented, with no shared framework connecting education, operational tooling, and certification. I led the design of AIQCI from the ground up, defining the system across its public platform and internal product layers. This included shaping the brand and evaluation model, designing dashboards, issue tracking and remediation workflows, and defining how results translate into certification.

The result is a structured system that transforms AI reliability into a measurable and governed process. Teams can evaluate performance, resolve issues, and maintain a verifiable record of reliability over time.