Nova: Chama

Demo

About

The problem it solves **Nova** is a decentralized AI agent that revolutionizes traditional group finance (Chama) by leveraging blockchain and AI. Nova tries to solve several problems in traditional group finance(Chama) systems. These problems include: - Lack of transparency in fund management - Manual tracking of contributions and distributions - Trust-based systems prone to mismanagement - Complex and time-consuming decision-making processes - Limited access to financial services **How does Nova make it better** - Pool funds securely. - Make contributions directly through their Ethereum wallets - Get real-time visibility of their contributions and pool status - Participate in fair, weighted voting on community decisions - Access loans with standardized terms and automated processing - Receive transparent interest distributions - AI-powered risk assessment for loans - Automated interest calculations and distributions - Secure end-to-end encrypted communication via XMTP **What are some of practical use cases of Nova:** a) Community Savings Groups: - Members can contribute regularly to a shared pool - Automated tracking of individual contributions - Fair distribution of interest based on contribution size - Transparent voting on how to use pooled funds b) Micro-lending Circles: - Members can request loans with 20% contribution as collateral - Standardized 10% interest rate for all loans - Automated loan processing and payment tracking - Fair access to credit based on contribution history c) Investment Clubs - Community can propose and vote on investment opportunities - Transparent tracking of investment performance - Fair profit sharing based on contribution size

How it's made

Challenges we ran into Some of the challenges we encountered included: 1. Human-in-the-Loop Decision Making Challenge Problem: Integrating human decision-making into an automated AI agent system Solution: Integrated Lang-graph human interaction model loop and Implemented a weighted voting system where voting power is proportional to contribution size. 2. AI Agent memory persistence in between messages Solution: Temporary story agent state and memory outside and injected when new messages came in to prevent agent from forgetting. 3. Group Setup and AI agent interaction - Took advantage of XMTP Group Conversations setup to intergrate the connected agent to the correct conversation with more that one user in the group setup