AI Chat Support
An AI-powered customer support chatbot using Google Gemini for context-aware responses, with real-time messaging and user authentication.
Tech Stack
Overview
AI Chat Support is an intelligent customer support chatbot that uses Google Gemini to deliver context-aware, conversational responses in real time. Built during the Headstarter SWE Fellowship, I owned both the front-end and back-end while collaborating with a teammate who contributed to the front-end UI.
The Problem
Traditional customer support relies on either human agents — expensive and limited by business hours — or rigid rule-based chatbots that frustrate users the moment a question falls outside their scripted decision trees. Customers expect instant, helpful answers, and businesses need a solution that scales without sacrificing quality.
The Solution
A chat interface backed by Google Gemini that understands context across a conversation, not just individual messages. Users authenticate for a personalized experience, send messages in a familiar chat UI, and receive intelligent responses instantly. The app handles follow-up questions, ambiguous phrasing, and multi-turn conversations — the scenarios where traditional chatbots break down.
Key Features
- Context-aware AI responses powered by Google Gemini, capable of handling multi-turn conversations and follow-up questions
- Secure user authentication through Firebase for personalized chat history and session management
- Real-time message display with typing indicators and delivery status for a native chat feel
- Persistent conversation history so users can return to previous support threads
- Clean, responsive chat interface built with Tailwind CSS
Technical Decisions
Why Google Gemini over OpenAI or Claude? Gemini offered a generous free tier that made the project viable for a fellowship setting where cost mattered. Its performance on conversational support tasks — maintaining context across turns and generating structured, helpful answers — matched what we needed without the token costs of GPT-4.
Why Firebase for the back end? Firebase Authentication and Firestore gave us real-time data sync and auth in a single platform. Chat applications demand instant message delivery, and Firestore’s real-time listeners eliminated the need to build a WebSocket layer from scratch — a significant time savings during a fellowship timeline.
How we split the work. I built the full-stack architecture — API routes for Gemini integration, Firebase auth flow, Firestore data modeling for conversations and messages, and the core chat logic. My teammate Calypso focused on the front-end UI components and styling. This split let us move in parallel without stepping on each other’s work.
What I Learned
Building a chat application sounds straightforward until you deal with the edge cases: messages arriving out of order, long AI responses that need streaming to feel responsive, and conversation context windows that grow with every turn. The Headstarter fellowship pushed us to ship on a tight timeline, which forced real prioritization — we scoped out features like suggested replies and conversation categorization to nail the core chat experience first. Working with Gemini’s API also deepened my understanding of prompt engineering for production use, where the difference between a good system prompt and a bad one is the difference between a helpful support agent and a confused one.