Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs

The rapid proliferation of AI applications has intensified the need for trusted, scalable, and policy-aligned infrastructures that enable small and medium-sized businesses (SMBs) to adopt AI responsibly. However, existing AI deployment pipelines remain fragmented across incentive management, content governance, recommendation engines, and operational observability, resulting in limited reusability and inconsistent compliance with emerging regulatory frameworks. To address these challenges, this paper proposes a unified trusted AI commercialization infrastructure that integrates incentive systems, multi-tenant governance controls, standardized content-moderation workflows, and interoperable recommendation APIs. Grounded in NIST AI RMF and ISO/IEC 42001 principles, the framework emphasizes regulatable reuse as a first-class objective ensuring that AI services can be replicated, governed, and expanded across SMBs with minimal friction. We design a multi-layer architecture featuring a policy-driven strategy center, cross-tenant observability fabric, trust-aligned feature store, and low-code API ecosystem, enabling automated compliance, transparent auditing, and scalable model orchestration. Experiments across diverse SMB scenarios demonstrate improved adoption efficiency, higher policy alignment, more stable governance outcomes, and measurable gains in recommendation performance and system trustworthiness. The proposed infrastructure provides a practical foundation for accelerating trusted AI commercialization at scale.