Building ReflectAI: An AI-Powered Reflective Journaling App with Google AI Studio and Firebase

Priyal Mangesh Mandloi — Wed, 02 Sep 2026 14:43:29 +0000

ReflectAI

ReflectAI is an AI-powered reflective journaling application designed to provide users with an interactive space for personal reflection and AI-assisted journal experiences.

The Idea Behind ReflectAI

Journaling can help people reflect on their thoughts and experiences, but a traditional journal cannot interact with the user or provide meaningful responses.

ReflectAI was built to make journaling more interactive by combining AI-powered conversations with a user-focused journaling experience.

The goal is to provide a space where users can write, reflect, and interact with AI in a more meaningful way.

Key Features

AI-powered reflective journaling
Gemini-powered interactions
User-focused interface
Firebase integration
Firestore database support
Secure environment variable configuration
Modern TypeScript-based application architecture

Technology Stack

ReflectAI uses a modern web application stack that includes:

Google AI Studio
Gemini API
Firebase
Cloud Firestore
TypeScript
Vite

Google AI Studio and Gemini

Google AI Studio was used as part of the development workflow for the application and its AI-powered functionality.

Gemini enables interactive AI experiences that can support users during their reflective journaling journey.

Instead of creating a static journaling experience, ReflectAI focuses on making user interaction more conversational and engaging.

Firebase and Firestore

Firebase provides important backend capabilities for the project.

Cloud Firestore is used as the database layer for storing and managing application data. The project also includes Firebase configuration and Firestore security rules as part of its codebase.

Using Firebase helps create a scalable foundation for an application where user data and application functionality need to be managed securely.

Project Architecture

The project follows a modern TypeScript-based structure.

The repository includes:

src/ for application source code
public/ for public assets
Firebase configuration
Firestore security rules
Environment variable configuration
Vite configuration
TypeScript configuration

This structure makes the project easier to maintain and extend with additional capabilities.

Building the Prototype

The prototype was developed as a customized application rather than simply keeping the initial starter experience unchanged.

The main focus was on creating an AI-powered reflective journaling experience with:

A clear user-focused interface
AI-powered interactions through Gemini
Firebase integration
Firestore database support
Secure configuration practices

Future Improvements

ReflectAI can be expanded further with additional capabilities such as:

More personalized AI reflection experiences
Advanced journal history and insights
Improved user authentication flows
Additional data visualizations
Notifications and reminders
More personalization features

Source Code

The public source code for ReflectAI is available on GitHub:

https://github.com/PriyalMMandloi/ReflectAI

Conclusion

ReflectAI explores how AI can make a traditional journaling experience more interactive and engaging.

By combining Google AI Studio and Gemini with Firebase and Firestore, the project provides a foundation for an AI-powered reflective journaling experience that can continue to evolve with additional features.

This project was built as part of the #AccelerateAIwithCloudRun challenge.

Building SpeakEasy AI: A Multilingual Voice Agent for Bharat

Priyal Mangesh Mandloi — Sat, 15 Aug 2026 16:39:43 +0000

Voice AI is becoming more accessible, but language and accessibility still remain important challenges for many users in India.

As part of the Murf AI Voice for Bharat Challenge, I built SpeakEasy AI — a voice-based AI assistant designed to help users practice English through natural conversations.

🚀 What is SpeakEasy AI?

SpeakEasy AI is a conversational voice agent that allows users to speak naturally and receive AI-generated voice responses.

The idea is simple:

Speak naturally. Practice confidently. Improve one conversation at a time.

The agent is designed around everyday English practice and supports English, Hindi, and Hinglish interactions.

🛠️ Tech Stack

The project uses:

Python
LiveKit Agents
Murf AI for voice generation
LLM-based conversation
Speech-to-text and text-to-speech
FastAPI
Next.js / frontend UI
Git & GitHub

🎙️ Voice Interaction

The main goal was to make the interaction feel like a real conversation rather than a traditional chatbot.

Users can start a voice conversation, speak naturally, and receive spoken responses from the AI agent.

I also integrated a friendly female voice to make the experience more natural and approachable.

🧠 Multilingual Conversation

One of the important parts of the project is handling different ways users communicate.

For example, a user can switch between:

English
Hindi
Hinglish

This makes the experience more comfortable for users who may not be completely confident speaking English.

🔄 Specialist Handoff

I also experimented with conversational routing.

For example, when a user asked for help with a mathematics problem, the main assistant could route the conversation toward a dedicated maths practice specialist.

This makes the architecture more flexible because different specialists can handle different tasks.

📊 Call Analytics

Another feature I implemented was a simple call analytics dashboard.

The dashboard tracks:

Total calls
Successful calls
Failed calls

This provides a basic view of how the voice agent is performing.

💡 What I Learned

Building SpeakEasy AI helped me understand that a voice agent is much more than simply connecting speech-to-text with an LLM.

Important parts include:

Natural conversation flow
Voice quality and latency
Multilingual interaction
Agent routing
Error handling
Call analytics
User experience

The biggest learning for me was understanding how different AI components work together to create a complete voice experience.

🇮🇳 Why Voice AI for Bharat?

India has a huge diversity of languages and communication styles.

Voice interfaces can make AI more accessible to people who may find traditional text-based interfaces difficult or less natural.

With better multilingual support and localized voice experiences, conversational AI can become much more useful for everyday learning and communication.

🚀 What's Next?

I plan to continue improving SpeakEasy AI by working on:

Better multilingual conversations
More specialist agents
Improved conversation memory
Better analytics
More real-world use cases

This project has been a great hands-on experience in building production-oriented AI voice applications.

Thanks to the Murf AI Voice for Bharat Challenge for providing the opportunity to explore voice AI more deeply.

DEV Community: Priyal Mangesh Mandloi