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This paper presents a high-level framework (vision) for utilizing big data analytics to harvest repositories of known good designs for the purpose of aiding mechanical product designs. The paper outlines a novel approach for applying artificial intelligence (AI) to the training of a mechanical design system model, assimilates the definition of meta-data for design containers (binders) to that of labels for books in a library, and represents customers, requirements, components and assemblies in the form of database objects with hierarchical structure. Design information can be harvested, for the purpose of improving design decision fidelity for new designs, by providing such database representation of the design content. Further, a retrieval model, that operates on the archived design containers, and yields results that are likely to satisfy user queries, is presented.