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Dhan-Niti is an AI-powered financial literacy app designed to empower rural women in India. It simplifies complex financial concepts into accessible and relatable modules, breaking down barriers to financial independence. With multilingual support, smart AI tools, and community-driven features, Dhan-Niti provides a one-stop platform for women to learn, manage, and grow their financial skills.
Through innovative AI integration, Dhan-Niti acts as a virtual mentor, offering budgeting tips, personalized advice, and motivational content in regional languages, ensuring no woman is left behind in her financial journey.
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Multilingual Financial Education:
- Courses available in Hindi, Tamil, Telugu, and other regional languages.
- Tailored modules for beginners to advanced learners, teaching budgeting, saving, and investing.
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AI-Powered Assistant:
- Personalized financial guidance and budgeting advice.
- Multilingual chatbot that makes learning interactive and conversational.
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Expense Tracker:
- Simplified tools for managing and tracking expenses.
- Offers insights into spending patterns to promote savings.
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Mentorship and Community Support:
- Connect with female mentors and successful entrepreneurs for guidance.
- Access real-life success stories for motivation and inspiration.
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News and Blogs:
- Curated multilingual financial news to keep users informed.
- Blogs and articles designed to inspire and educate.
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AI-Driven Storytelling:
- Inspiring financial stories integrated with real-world lessons to motivate users.
To build the prototype, we leveraged:
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React Native with Expo:
A cross-platform framework that ensures smooth functionality on Android and iOS devices. -
Rapid API:
Used to prototype the AI-powered chatbot for multilingual and contextual financial guidance. -
Cloud Firestore:
A NoSQL database for seamless offline support and asynchronous data storage. -
Clerk Authentication:
For secure and hassle-free onboarding with user identity management.
As we advance, Dhan-Niti will evolve with:
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Agentic AI Technology:
- Integrating agent-based AI to create more autonomous and proactive interactions.
- Users will experience a chatbot that not only answers questions but also predicts needs and recommends solutions.
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OpenAI GPT Models with Vector Database:
- Enhanced personalization through advanced AI models.
- A vector database (e.g., Pinecone) will enable user history tracking, contextual learning, and tailored recommendations.
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Enhanced Analytics and Gamification:
- Introduce analytics dashboards for users to visualize their financial progress.
- Gamify the learning experience to increase engagement and motivation.
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Improved Offline Support:
- Expand offline capabilities, ensuring continuous access to educational modules even in low-connectivity areas.
- Node.js and npm installed on your system.
- Install Expo CLI globally:
npm install -g expo-cli
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Clone the Repository:
git clone https://github.com/<your-repo-url>.git cd Dhan-Niti
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Install Dependencies:
npm install
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Run the App:
expo start
- Use the QR code displayed in the terminal to open the app on your smartphone via the Expo Go app.
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Set Up Firebase:
- Create a Firebase project and add the configuration files:
google-services.json
for Android.GoogleService-Info.plist
for iOS.
- Create a Firebase project and add the configuration files:
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Configure Clerk Authentication:
- Set up a project on Clerk and add your API keys to the
.env
file.
- Set up a project on Clerk and add your API keys to the
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News API:
Fetches multilingual financial news and blogs for the users. -
Rapid API:
Powers the AI chatbot for financial advice and budgeting tips in multiple languages. -
Custom APIs:
Manage user-specific data like courses, mentors, and progress tracking.
Future API Enhancements:
We aim to reduce dependency on third-party APIs by developing in-house solutions powered by OpenAI and agentic AI technology for better scalability, personalization, and control.
We welcome developers, collaborators, and organizations to join us in expanding Dhan-Niti. Feel free to open issues, submit pull requests, or suggest features.
This project is licensed under the MIT License.