Small businesses often discover cash flow problems only after they become critical. Existing financial tools focus on reporting past transactions, but very few help businesses anticipate future risks and take preventive action. We wanted to build an intelligent financial agent that not only predicts cash shortages before they occur but also recommends actionable solutions to avoid them.
What it does
CashSense AI is an autonomous cash flow crisis prevention agent designed for small businesses. It analyzes financial transaction data, identifies spending patterns, forecasts future cash positions for the next 30, 60, and 90 days, and proactively alerts users about potential cash flow risks.
The system goes beyond prediction by generating actionable recommendations, such as identifying unnecessary expenses, highlighting late-paying customers, and suggesting strategies to improve financial stability.
How we built it
CashSense AI uses a multi-agent architecture where specialized AI agents collaborate to perform different financial tasks:
- Data Agent collects and processes transaction data.
- Analysis Agent identifies spending patterns and financial risks.
- Prediction Agent forecasts future cash positions.
- Alert Agent detects potential cash flow crises.
- Action Agent recommends preventive measures and generates financial guidance.
The agents are orchestrated using LangGraph, powered by Gemini, and connected to MongoDB Atlas for persistent storage and historical analysis. A Streamlit interface enables users to upload financial data and view insights through an intuitive dashboard.
Challenges we ran into
One of the biggest challenges was designing an effective multi-agent workflow where each agent could independently contribute insights while maintaining a seamless flow of information. Building reliable forecasting logic, integrating multiple technologies, and ensuring clear financial recommendations were also key challenges.
Accomplishments that we're proud of
- Built an end-to-end autonomous financial intelligence system.
- Implemented a multi-agent architecture for financial analysis.
- Developed cash flow forecasting capabilities.
- Integrated AI-driven recommendations instead of simple reporting.
- Created a practical solution for a real-world business problem.
What we learned
This project deepened our understanding of multi-agent systems, financial forecasting, AI orchestration, and building production-ready applications using cloud-native technologies. We also learned how AI agents can move beyond conversation and actively support decision-making.
What's next for CashSense AI
Future enhancements include real-time bank integrations, advanced forecasting models, automated customer payment follow-ups, personalized financial planning, and deeper business intelligence capabilities to help organizations make smarter financial decisions.
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for CashSense AI
Built With
- docker
- fastapi
- gemini
- github
- google-cloud
- langgraph
- mongodb-atlas
- mongodb-mcp
- python
- streamlit
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