As businesses scale, customer support becomes a critical yet challenging operation. Traditional support systems often struggle with increasing query volumes, leading to long wait times, inconsistent responses, and rising operational costs. To tackle these issues, I developed an AI-powered multi-agent chatbot that automates customer inquiries in real time, ensuring fast, accurate, and scalable support.

Tech Stack

The chatbot is powered by:

• FastAPI – Backend framework for handling chatbot requests efficiently.
• LangGraph – Manages multi-agent workflows for structured task delegation.
• Pinecone – Vector database for fast retrieval of FAQs and policies.
• Streamlit – Interactive UI framework for customer interactions.

How It Works

This chatbot is built on a multi-agent architecture with specialized agents collaborating under a structured framework:

• SQL Agent – Retrieves real-time customer data, such as order statuses, restaurant details, and payment records, by interacting with a PostgreSQL database.
• RAG Agent – Extracts relevant information from company policies, FAQs, and terms of service using Retrieval-Augmented Generation (RAG) to provide precise, up-to-date responses.
• Coordinator – Analyzes customer queries and assigns tasks to the appropriate agents for optimal efficiency.
• Representative – Synthesizes responses and communicates them professionally to users, mimicking a real customer support representative.

This system ensures that users receive instant, context-aware responses without requiring human intervention, significantly reducing operational costs while enhancing the customer experience.

Why It Matters

By replacing traditional customer support with AI-driven automation, businesses can:

✔ Reduce response times from minutes to seconds
✔ Lower customer support costs by minimizing the need for human agents
✔ Improve customer satisfaction through instant, accurate, and professional responses
✔ Scale effortlessly as customer demand grows

This project has several real-world applications across various industries:

• Food Delivery Services (Uber Eats, Foodpanda, DoorDash, etc.) – Automates queries related to orders, refunds, and delivery issues.
• E-Commerce Platforms – Handles product inquiries, shipping details, and return policies.
• Telecom & Utilities – Assists customers with billing queries, service upgrades, and troubleshooting.

Final Thoughts

This project showcases the power of AI-driven automation in customer service and highlights my expertise in LLMs, SQL integration, and multi-agent architectures. As AI continues to reshape customer interactions, solutions like this will play a crucial role in making support systems smarter, faster, and more reliable.

Check out the project on GitHub.
Curious to learn more? Let’s connect!