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Product Requirements Document: AI-Powered Customer Service System

1. Introduction

This document outlines the requirements for an AI-powered Customer Service System designed to enhance customer support, improve response times, and reduce operational costs for small businesses. The system will utilize a Multi-Agent System (MAS) architecture, leveraging LangChain for agent orchestration and Gemini as the underlying Large Language Model (LLM). Docker will be used for containerization, ensuring consistent and reproducible deployments across different environments.

2. Goals

  • Reduce customer service operational costs by 40% within the first year of deployment.
  • Improve average customer satisfaction (CSAT) score by 15% within the first year.
  • Achieve a First Contact Resolution (FCR) rate of 80% for common customer inquiries.
  • Provide 24/7 customer support availability.
  • Reduce average response time to under 1 minute for chat and email inquiries.

3. Target Users

  • Primary Users:

    • Customers: Individuals seeking assistance with products or services.
    • Customer Service Managers: Individuals responsible for overseeing the customer service team and monitoring system performance.

  • Secondary Users:

    • Business Owners: Individuals seeking to improve customer service efficiency and reduce costs.

4. Product Overview

The AI-Powered Customer Service System will be a multi-agent system composed of specialized agents designed to handle various aspects of customer service. It will integrate with common communication channels (website chat, email, social media) and existing business systems (CRM, order management). The system will be containerized using Docker to ensure portability and ease of deployment.

5. Key Features

5.1 Multi-Agent System (MAS) Architecture

  • Agent Orchestration: LangChain will be used to manage and coordinate the interactions between different agents.
  • Agent Communication: Agents will communicate and share information to resolve complex issues collaboratively.
  • Agent Specialization: Each agent will be designed with a specific role and expertise.

5.2 Core Agents

  • Greeter Agent (Chatbot):

    • Functionality:
      • Initiates conversations with customers on the website or other chat platforms.
      • Collects basic information (name, email, issue type).
      • Provides instant answers to frequently asked questions (FAQs).
      • Routes complex inquiries to appropriate specialized agents.
    • Technology: LangChain, Gemini, (potentially) a vector database for FAQ retrieval.

  • Email Management Agent:

    • Functionality:
      • Automatically categorizes incoming customer emails.
      • Prioritizes emails based on urgency and sentiment.
      • Generates automated responses for common inquiries.
      • Routes complex or urgent emails to human agents or specialized agents.
    • Technology: LangChain, Gemini, email API integration.

  • Order Management Agent:

    • Functionality:
      • Provides information about order status, shipping details, and delivery timelines.
      • Processes returns and refunds.
      • Handles order modifications (e.g., address changes).
    • Technology: LangChain, Gemini, integration with order management system/database.

  • Product/Service Information Agent:

    • Functionality:
      • Provides detailed information about products or services.
      • Answers questions about features, specifications, pricing, and availability.
      • Offers product recommendations based on customer needs.
    • Technology: LangChain, Gemini, (potentially) a vector database or knowledge base of product information.

  • Sentiment Analysis Agent:

    • Functionality:
      • Analyzes customer messages (chat, email, social media) to determine sentiment (positive, negative, neutral).
      • Flags negative sentiment for priority handling by human agents.
      • Provides insights into overall customer satisfaction.
    • Technology: LangChain, Gemini, (potentially) specialized sentiment analysis libraries.

  • Escalation Agent:

    • Functionality:
      • Monitors agent conversations and identifies situations requiring human intervention (e.g., complex issues, angry customers).
      • Seamlessly transfers the conversation to a human agent with full context.
    • Technology: LangChain, Gemini, integration with live chat platform/ticketing system.

  • Feedback Collection Agent:

    • Functionality:
      • Proactively solicits customer feedback through surveys or feedback forms.
      • Analyzes feedback data to identify areas for improvement.
      • Generates reports on customer satisfaction trends.
    • Technology: LangChain, Gemini, integration with survey platform/email system.

5.3 Human-in-the-Loop System

  • Seamless Escalation: Agents should be able to escalate complex issues to human agents without interrupting the customer experience.
  • Contextual Handoff: When escalating, the system must provide the human agent with the full conversation history and relevant customer information.
  • Agent Monitoring Dashboard: A dashboard for customer service managers to monitor agent performance, identify bottlenecks, and intervene when necessary.

6. Technical Requirements

  • LLM: Gemini (Pro or Ultra, depending on complexity and volume).
  • Agent Framework: LangChain.
  • Programming Language: Python.
  • Containerization: Docker.
  • Cloud Platform: Google Cloud Platform (GCP) preferred for seamless Gemini integration. (Can be adjusted based on preference).
  • Databases:
    • Vector database (e.g., Pinecone, Weaviate) for efficient knowledge retrieval (optional, for FAQ and product information).
    • Relational database (e.g., PostgreSQL, MySQL) for storing customer data, conversation history, and other structured data.
  • APIs:
    • Gemini API.
    • Integration APIs for communication channels (e.g., Twilio, email providers).
    • Integration APIs for business systems (CRM, order management).

7. Non-Functional Requirements

  • Performance:
    • Response times for chatbot interactions should be under 1 second.
    • Email responses should be generated within 5 minutes.
  • Scalability: The system should be able to handle a growing volume of customer interactions without performance degradation.
  • Security: Customer data must be handled securely and in compliance with relevant data privacy regulations (e.g., GDPR, CCPA).
  • Reliability: The system should be available 24/7 with minimal downtime.
  • Maintainability: The codebase should be well-documented, modular, and easy to maintain and update.
  • Portability: The use of Docker ensures the system can be easily deployed and run consistently across different environments (development, staging, production).

8. Release Criteria

  • All core agents are fully functional and integrated.
  • The system meets the defined performance, scalability, security, and reliability requirements.
  • User acceptance testing (UAT) has been completed successfully.
  • Documentation (user manual, API documentation, Docker setup instructions) is complete.
  • System is fully containerized with Docker, and deployment instructions are documented.

9. Future Enhancements

  • Proactive Support: Agents could proactively reach out to customers who might be experiencing issues based on their website activity or other data.
  • Personalized Recommendations: Agents could offer personalized product or service recommendations based on customer purchase history and preferences.
  • Multilingual Support: Expand language capabilities beyond English.
  • Voice Integration: Integrate with voice assistants (e.g., Google Assistant, Alexa).
  • Advanced Analytics: Implement more sophisticated analytics to track customer behavior, identify trends, and optimize agent performance.

10. Open Issues

  • Specific integrations with existing CRM and order management systems need to be finalized.
  • The choice of a vector database (if needed) requires further evaluation.
  • Detailed metrics for agent performance monitoring need to be defined.
  • Specific Docker image optimization strategies need to be determined.

11. Docker Specifics

  • Dockerfile: A Dockerfile will be created to define the application's environment, dependencies, and runtime instructions.
  • Docker Compose: A docker-compose.yml file will be used for defining and managing the multi-container application (e.g., application server, database).
  • Image Optimization: The Docker image will be optimized for size and performance, using techniques like multi-stage builds and appropriate base images.
  • Registry: A container registry (e.g., Docker Hub, Google Container Registry) will be used to store and manage the Docker images.

This PRD provides a comprehensive framework for the development of an AI-powered Customer Service System. It outlines the key features, technical requirements, and goals of the project, setting the stage for a successful implementation using LangChain, Gemini, and Docker.