budgetbot

Inspiration

Managing personal finances can be overwhelming, especially with multiple expenses and sources of income. We wanted to build an intelligent solution that provides real-time financial insights using advanced AI technology. The idea was to empower users to make informed financial decisions effortlessly.

How we built it

• Frontend: Developed using Next.js and Tailwind CSS for a clean and responsive UI.
  
• Backend: Built with Node.js and Express for API management.
  
• Data Processing: Pathway was used for real-time data ingestion and analysis.
  
• AI Agents: Integrated Fetch AI’s agentic network to fetch and analyze transaction data.
  
• RAG: Implemented an Agentic RAG system for personalized financial query responses using financial data embeddings.
  
• Database: MongoDB for storing user data and financial history.
  

Challenges we ran into

• Integrating real-time data streams with Pathway without data loss.
  
• Developing efficient AI agents using Fetch AI for financial decision-making.
  
• Fine-tuning the RAG model to ensure accurate and context-aware responses.
  
• Ensuring data security and privacy while managing sensitive financial information.
  

Accomplishments that we're proud of

• Successfully built a scalable platform with real-time financial data tracking.
  
• Developed AI agents that provide actionable insights based on spending patterns.
  
• Achieved smooth integration of Pathway and Fetch AI for real-time processing.
  
• Enhanced user experience with a responsive and intuitive UI.
  

What we learned

• Advanced use cases of Fetch AI’s autonomous agents in financial applications.
  
• Efficient real-time data management using Pathway.
  
• The importance of personalized AI in providing meaningful financial insights.
  
• Best practices for handling and securing financial data.
  

What's next for BudgetBot

• Implement predictive analysis to forecast future expenses and provide budgeting suggestions.
  
• Expand support for more financial institutions for seamless data integration.
  
• Enhance the RAG model to offer even more personalized financial advice.
  
• Develop mobile app support for improved accessibility.
  
• Introduce community-driven financial tips and insights using AI-powered recommendations.
  

Built With

• fetchai
• nextjs
• pathway
• postman
• tailwind