Test organizations collect rich facility data, but reuse is limited because measurements lack the setup context needed to trust and compare results: metadata is scattered across tools and individuals. Quix presents a two-agent architecture that makes test data searchable: a data engineering agent attaches configuration context as data is captured, building a queryable catalog; an analysis agent answers engineers’ questions end-to-end by selecting runs, generating analysis and visuals and refining via clarifying questions. Outputs can be saved and shared as apps. Proven in Formula 1 and industrial OEM deployments, it reduces redundant testing and cuts time-to-answer from days to minutes.

• Why test data isn’t reused: missing setup context makes prior results hard to trust, so teams re-test
• How to build a search engine for test data by auto-attaching configuration metadata at capture
• How AI agents answer engineering questions end-to-end (find runs → analyze → visualize → iterate)
• How just-in-time software removes data-tool friction so domain engineers self-serve insights
• What impact looks like in practice: fewer redundant tests and faster time-to-answer (days → minutes)