Introducing Axlerod: An LLM-based Chatbot for Assisting Independent Insurance Agents

TL;DR

This paper introduces Axlerod, an AI-powered chatbot designed to assist independent insurance agents by improving operational efficiency through advanced NLP and knowledge integration, demonstrating high accuracy and reduced search times.

Contribution

The paper presents the design, implementation, and empirical evaluation of Axlerod, a novel AI chatbot tailored for insurance agents that combines NLP, RAG, and domain knowledge for improved performance.

Findings

Achieved 93.18% accuracy in policy retrieval

Reduced search time by 2.42 seconds

Demonstrated robustness in real-time, context-aware responses

Abstract

The insurance industry is undergoing a paradigm shift through the adoption of artificial intelligence (AI) technologies, particularly in the realm of intelligent conversational agents. Chatbots have evolved into sophisticated AI-driven systems capable of automating complex workflows, including policy recommendation and claims triage, while simultaneously enabling dynamic, context-aware user engagement. This paper presents the design, implementation, and empirical evaluation of Axlerod, an AI-powered conversational interface designed to improve the operational efficiency of independent insurance agents. Leveraging natural language processing (NLP), retrieval-augmented generation (RAG), and domain-specific knowledge integration, Axlerod demonstrates robust capabilities in parsing user intent, accessing structured policy databases, and delivering real-time, contextually relevant responses. Experimental results underscore Axlerod's effectiveness, achieving an overall accuracy of 93.18% in policy retrieval tasks while reducing the average search time by 2.42 seconds. This work contributes to the growing body of research on enterprise-grade AI applications in insurtech, with a particular focus on agent-assistive rather than consumer-facing architectures.