Contextual Quoting Agentic System

This project implements a sophisticated multi-agent system for generating personalized, context-aware quotes for complex products and services. It leverages LangChain/LangGraph to create an intelligent quoting workflow that goes beyond traditional pricing methods by incorporating: - Retrieval-Augmented Generation (RAG) for context-sensitive data retrieval - Multi-Agent Architecture with specialized roles: - Main Assistant: Initial information gathering - Underwriting Assistant: Risk evaluation - Quote Assistant: Premium calculation - Intelligent Classification using sentiment analysis and business context - Dynamic Workflow Management through a state graph system - Database Integration for storing and retrieving category rates

Traditional quoting systems often struggle with complex products and services where pricing depends on multiple interrelated factors. Current solutions typically: - Rely heavily on manual intervention - Have limited ability to consider context - Struggle with non-standard cases - Lack consistency across different underwriters - Cannot easily adapt to changi…