Hitesh Acharya 02 05 Enterprise B2B sales intelligence has historically relied on manual research processes that consume disproportionate analyst time relative to value delivered This paper presents a comprehensive architectural framework for deploying AIaugmented sales intelligence systems at scale across Fortune 500 environments Drawing on realworld deployment experience with the 9Lenses platform AWS Bedrock infrastructure and LangChainbased orchestration we demonstrate how retrievalaugmented generation RAG pipelines combined with agentic workflow design can reduce sales research cycle times from an average of four weeks to approximately thirty minutes while simultaneously improving output quality and analytical depth The paper details system architecture decisions embedding strategies prompt engineering methodologies evaluation metrics and organisational change management considerations We present empirical performance data from production deployments including latency benchmarks accuracy evaluations and user satisfaction metrics across multiple enterprise clients Our findings suggest that the convergence of foundation models vector search infrastructure and domainspecific finetuning creates a viable path toward autonomous sales intelligence that is both commercially scalable and analytically rigorous Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026