Multi-Agent Customer-Support System

-20%

Multi-Agent Customer-Support System

0 Orders 0 Wish listed

₹4,999.00

Qty

Total price:

₹4,999.00

Overview
Reviews

Detail Description

1. Abstract

The Multi-Agent Customer-Support System is a Generative AI-powered application that uses multiple specialized AI agents to automate customer service tasks. Each agent handles specific responsibilities, such as understanding customer queries, retrieving knowledge-base information, tracking orders, troubleshooting issues, and escalating complex cases. Using Large Language Models (LLMs) and agent coordination, the system provides relevant responses, improves response times, and reduces manual customer-support workload.

2. Objectives

Automate customer-support interactions using AI agents.
Assign customer queries to specialized agents.
Provide accurate answers using a knowledge base.
Support order tracking and troubleshooting workflows.
Maintain conversation history and customer context.
Escalate complex issues to human support staff.
Improve response speed and customer satisfaction.

3. Existing System

Traditional customer support relies on human agents, static FAQs, and basic chatbots to resolve customer queries.

Disadvantages

Long waiting times during busy periods.
Limited understanding of complex customer questions.
Repetitive manual work for support staff.
Difficulty handling multiple support workflows.
Basic chatbots may provide generic responses.

4. Proposed System

The proposed system coordinates multiple AI agents, each responsible for a specific support function.

Key features:

Customer chatbot with natural-language understanding.
Query-classification and routing agent.
Knowledge-base retrieval agent.
Order and account-support agent.
Troubleshooting and solution-recommendation agent.
Response-generation agent.
Human escalation for unresolved issues.
Conversation history and ticket management.
Admin dashboard for monitoring agents and support activity.

5. Implementation Procedure

Collect customer FAQs, product information, and support documents.
Build a chatbot interface using Streamlit or React.js.
Integrate an LLM for understanding customer queries.
Create specialized agents for classification, retrieval, troubleshooting, and response generation.
Implement an orchestration layer to coordinate agent workflows.
Connect a knowledge base using RAG and a vector database.
Integrate mock order and ticket-management APIs for demonstrations.
Add conversation memory and escalation workflows.
Test response accuracy, routing, and multi-agent coordination.
Deploy the application and monitor its performance.

6. Software Requirements

Programming Language: Python
Frontend: Streamlit or React.js
Backend: FastAPI or Flask
LLM: OpenAI API or a compatible model
Agent Framework: LangGraph or CrewAI
RAG Framework: LangChain
Vector Database: FAISS or ChromaDB
Database: PostgreSQL or MongoDB
Development Environment: VS Code
Version Control: Git and GitHub
Operating System: Windows or Linux

7. Hardware Requirements

Processor: Intel Core i3 or equivalent
RAM: Minimum 8 GB; 16 GB recommended
Storage: At least 10 GB free space
Internet: Required for hosted LLM APIs
GPU: Optional when using cloud-based AI models

8. Advantages of the Project

Automates repetitive customer-support tasks.
Routes queries to specialized AI agents.
Provides context-aware responses.
Reduces response times and manual workload.
Supports multiple customer-support workflows.
Enables human escalation for complex issues.
Improves support monitoring through dashboards.
Can be extended to different business domains.

No review given yet!

Fast Delivery all across the country

Safe Payment

7 Days Return Policy

100% Authentic Products

Shopping cart