Agentic AI for Natural Language Query Interface in Intelligent Customer Success Management- Conversational Analytics and Automated Reporting

Abstract

Today's CSM is no longer about relying on that manual account monitoring or that reactive customer engagement—it has become a proactive, data powered discipline, all thanks to data analytics. However, current customer success applications are limited by static dashboards, pre-written queries and report templates, and an inflexible reporting infrastructure that does not support the ad hoc, dynamic, and multi-dimensional analytics required by today’s customer success professionals. Such legacy systems require trained personnel to derive actionable insights, which can lead to bottlenecks that delay crucial decisions and hinder organizational agility. As enterprise customer portfolios become increasingly complex, and the pressure to decrease churn increases, the urgency to have systems that make customer insights accessible to all, with real-time interventions becoming the norm, is ever higher.

In this paper, we present the development of a new Agentic AI framework for a Natural Language Query (NLQ) Interface for intelligent customer success management. The software allows business users to conduct "big data" analysis and create reports from corporate data stores using natural language, Enter Works said. In addition, with the deployment of a simplified 3-agent architecture (i.e., Query Processing Agent QPA, Data Integration Agent (DIA), and Response Generation Agent RGA), the architecture eliminates the need for humans-in-the-loop. It allows for more or less translation-based, application independent data integration and response generation. Agents collaborate to handle multi-turn dialogues, disambiguate ambiguous user inputs, access pertinent data across customer relationship management (CRM) systems and file-based systems, and produce executive-quality analytic outputs, such as sentiment insights, risk scores, ARR trends, and proactive recommendations.

Leveraging a domain-adapted transformer-based NLP pipeline, the system attains 94.7% query accuracy and 96.5% precision in entity recognition on customer profile queries, revenue forecasts, and risk assessments. Sentiment analysis capabilities include multi-channel communication such as emails,meeting transcripts, and support interactions, which enable the real-time detection of dissatisfaction or renewal risk with more than 94% accuracy.Experimental deployment in real enterprise environments in healthcare SaaS and financial services—handling more than 2,300 natural-language queries from 423 customers and $21M ARR—leads to substantial operational benefits. Notably, the NLQ system decreased the average query response time by 99.7%, increased the number of queries answered per user-hour by 484%, and reduced the time to generate reports from hours to minutes. In the same breath, support dependency was reduced by more than 90%, while proactive customer interventions increased by 45%, resulting in an annual ROI of over $1.56M across both environments.

This paper also provides an achievable and scalable architecture to integrate conversational AI into enterprise-level CSM practices. Unlike other NLP platforms that exist today, our domain-specific approach combines CRM metadata, financial KPIs, engagement metrics, and sentiment signals in a unified,user-friendly interface, enabling business users at all levels to succeed with minimal instructional support or training. Compared to the uncomplicated agentic model, the simplified agentic model also enhances the practicality of implementation without compromising the integrity of the analysis. Future work is discussed next, including cross-domain adaptation, multimodal input processing (e.g., voice queries), explainable AI functionalities for customer insights, and federated learning that ensures enterprise data privacy requirements. By addressing the chasm between data complexity, user-friendly analytical capabilities, and access, this work paves the way for an entirely new class of intelligent, agentic business intelligence systems specifically designed for customer success management in the age of conversational computing.