Kater is an AI-driven customer support agent that automates responses and assists users with queries. It uses machine learning to understand customer intents, delivering personalized interactions and resolving questions efficiently. Kater reduces the workload on human agents by handling routine inquiries and providing 24/7 availability, ensuring that customers receive timely assistance at any hour. Its ability to learn from previous interactions allows it to improve its responses over time, creating a tailored support experience.

This project showcases a fully functional customer service AI agent that leverages OpenAI’s Autogen framework and a Streamlit front end. It routes user inquiries through a customizable agent pipeline, maintains conversational context, and generates accurate, context-aware responses. Developers can easily clone the repository, set their OpenAI API key, and launch a web UI to test or extend the bot’s capabilities. The codebase includes clear configuration points for prompt design, response handling, and integration with external services, making it a versatile starting point for building support chatbots, helpdesk automations, or internal Q&A assistants.

Faye is a self-learning AI chatbot specializing in Web3 customer success. It masters smart contracts to provide accurate, relevant, and on-chain troubleshooting support. The platform automates up to 65% of customer support tickets, reducing the workload for human support teams and enhancing efficiency. Faye continuously learns from interactions and improves its support capabilities, making it an indispensable tool for Web3 businesses aiming to deliver exceptional customer service.