Authors: Sushanth.Chandrashekar, Shantanu Nagaraj, Ashish Naidu
Abstract: The tender bidding process remains a critical yet inefficient cornerstone of global procurement, plagued by manual document analysis, compliance errors, and resource-intensive workflows. This paper introduces Agentic Graph RAG, an innovative AI system that redefines bid preparation by integrating Retrieval-Augmented Generation (RAG), dynamic knowledge graph and multi-agent collaboration to automate and optimize the end-to-end bidding pipeline. Our architecture combines three transformative pillars: a cognitive document processor, a living knowledge graph, and a specialized agent framework. Validated on real-world tenders, the system demonstrates 98% clause extraction accuracy, 80% faster bid preparation, and a 6.4x ROI through compliance assurance and strategic positioning. This work bridges cutting-edge AI research with practical procurement challenges, offering a scalable blueprint for intelligent automation in competitive bidding.
