A Real-World Deployment of an Enterprise Conversational AI Platform for Demand Generation and Lead Generation Using Guided Workflows with a Rasa-Based Chatbot

Abstract

Enterprise organizations increasingly employ conversational AI platforms to support marketing and sales operations, particularly in demand generation and lead qualification workflows. While research on conversational systems has advanced rapidly [1], [2], comparatively little work has documented how such systems are architected and evaluated in real-world enterprise deployments that directly influence revenue-generating business processes. This paper presents a comprehensive technical and operational analysis of an enterprise conversational AI platform built on the Rasa framework and deployed to support demand generation and lead generation through guided conversational workflows. The system is designed to capture user interest in products, answer product-related questions, qualify prospects, and schedule sales demonstrations. Rather than reporting proprietary performance figures, the paper defines a rigorous metrics framework for evaluating business impact, including engagement effectiveness, lead quality indicators, conversion funnel movement, and return-on-investment (ROI) considerations grounded in established marketing analytics and MLOps practices [4], [7]. The paper focuses on system architecture, workflow orchestration, measurement methodology, and operational challenges encountered in production. The goal is to provide a reproducible technical reference for organizations designing revenue-oriented conversational AI systems while avoiding unverifiable empirical claims.