1.1. The Procurement Dilemma: Enterprise procurement generally evaluates AI vendors on commercial metrics (cost, speed, capability) while overlooking the hidden liabilities of irresponsible deployment. Conversely, global regulatory frameworks remain static and legalistic, offering little quantitative mechanism for daily vendor comparison on governance, sustainability, and responsibility. 1.2. Chhapra’s Framework for Responsible AI Procurement (C-RAP): C-RAP, the proposed framework, establishes a calculated, ready-to-implement multi-domain scoring system to evaluate AI vendors on governance, human harm, accountability, market power, and data ethics, complementing baseline legal compliance. 1.3. Soft Power & Behavioral Nudging: The objective is to intelligently monitor technological advancement, not halt it. Applying C-RAP uniformly creates a "like-to-like" comparative baseline. Under reputational and procurement pressure, "Unsatisfactory" vendors are structurally incentivized to elevate their responsibility standing. 1.4. Dual-Use Utility & Hard Truths: While designed for external vendor selection, C-RAP applies equally to internal AI development, strengthening self-governance and providing a verifiable Unique Selling Point (USP). Even if not utilized as a full scoring matrix, its architecture serves as an internal strategic diagnostic during vendor dialogues. 1.5. Purpose & Core Logic: C-RAP operates as a non-commercial responsibility layer. Its core premise is absolute: risks involving human harm, irreversible damage, and systemic failure cannot be traded off against price, speed or evolution.