Authors - Tirupathi Rao Dockara, Manisha Malhotra Abstract - AI and data platforms are increasingly expected to deliver end-to-end business automation under rapid market and regulatory change. However, prevailing platform construction strategies remain predominantly top-down: teams standardize a generic capability stack and subsequently customize it for heterogeneous domains through code, integration glue, and service forks. This approach amplifies technical debt, fragments governance, and makes continuous adaptation expensive. This paper introduces the Inverse Vertex Pyramid (IVP), a design pattern that reverses the direction of platform derivation. IVP begins at the use-case vertex by conducting rigorous analysis of high-value specialized automation scenarios and generalizes them into explicit, machine-actionable platform descriptors (metadata models, domain ontologies, policy/workflow specifications, and capability contracts) that form a stable, reusable core. Specialization is realized primarily via declarative configuration and policy changes, rather than code rewrites. We formalize IVP as a pattern, propose a reference architecture separating control and execution planes, and provide a comparative analysis against layered architectures, domain-driven design, and microservice platforms. A proof-of-concept walkthrough in regulated claims automation illustrates the generalization mechanism and highlights how IVP can reduce re-engineering, improve governance consistency, and accelerate time-to-market. The paper concludes with limitations, threats to validity, and a research agenda for automated use-case mining, formal verification of policies, and quantitative evaluation of platform agility.
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