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ABSTRACT Electronics design teams face a paradox: while design-rule checks and expert experience promise flawless boards, most products require multiple design iteration cycles until achieving the reliability requirements and product quality. These iterations require physical samples and expensive testing procedures to finally uncover risks that surface only in the laboratory. At the same time, up to five million skilled German manufacturing workers will retire by 2030, taking decades of experiential knowledge with them. Hence, the cost for traditional “design then test” paradigms and time to market will increase. Reliability Intelligence (RI) is proposed as a new approach that unites design, manufacturing process and quality data, enabling continuous, statistically rigorous feedback between the lab to design department. RI extends the concept by pairing structured laboratory telemetry with reliability-growth analytics—e.g. the Crow-AMSAA model, which quantifies how each corrective action shifts the failure-rate slope over cumulative test time. Thus RI provides the data foundation for data driven decision making. RI requires four layers. A Lab Ingest layer captures thermal, mechanical and electrical stress, and the inspection data e.g. optical or x-ray inspection as the basis of traceability in the laboratory. A est-Data storage stores the data with immutable traceability keys. On top, a machine learning enhanced Reliability-Growth Engine computes MTBF, β-slope and B10 life without manual, error-prone data evaluation overhead, flagging anomalous trends. Finally, a Design-Feedback API streams insights to ECAD plugins and PLM dashboards, allowing engineers to compare variant A vs. B against historical fleets with a single click. By fusing these data streams with statistical reliability models, RI turns isolated lab evidence into organisation-wide learning. This results in shorter verification loops and faster compliance reporting, because analysis reports can be regenerated from structured data instead of manual spreadsheets. Moreover, prototype counts can be reduced, yielding significant material and labour savings, while agile, data-driven workflows trim overall time-to-market for complex modules. Unlike conventional design-then-test approaches—valuable but inherently human experience-bound—Reliability Intelligence is empirical. It captures how real boards behave under real stresses, transforming every test hour into searchable evidence. As the electronics sector contends with shrinking product cycles and a looming expertise gap, RI offers a scalable path to safeguard know-how, cut iteration cost, raise field reliability and serve a court-proof, traceable documentation to prevent business risk.