Algorithmic Public Procurement: Deploying Serverless Cloud Middleware for AI-Driven Vendor Evaluation and Supply Chain Integrity

The digitalization of public procurement presents a critical opportunity to enhance governmental efficiency, transparency, and supply chain performance. However, integrating artificial intelligence into public sector contracting is frequently bottlenecked by legacy institutional architectures and static data silos. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for digital procurement. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting algorithms, the proposed system programmatically ingests vendor performance telemetry, bidding metadata, and supply chain friction indicators. The system translates these inputs into a dynamic Vendor Integrity and Performance Score, instantly identifying procurement anomalies and corruption risks. Preliminary architectural evaluations demonstrate that decoupling the AI evaluation layer from legacy government databases significantly reduces the latency of contract auditing, providing public management researchers with a deterministic, highly scalable tool for analyzing digital procurement and institutional performance.