Oraclend
NFT-collateralized lending protocol for RWA
About
The problem it solves Traditional real estate lending is slow, opaque, and often inaccessible, requiring extensive paperwork, manual risk assessments, and trusted intermediaries. Borrowers face high barriers to entry, while lenders struggle to access transparent, diversified investment opportunities. Cross-border lending is even more complex, with additional friction and risk. ORACLEND solves these problems by providing a decentralized, NFT-collateralized lending platform for real-world assets (RWAs): - Tokenized Real Estate: Property owners can mint NFTs representing their real estate, unlocking liquidity without selling their assets. - Instant, Transparent Loans: Borrowers use their property NFTs as collateral to request loans, with all terms and transactions recorded on-chain for full transparency. - AI-Powered Risk Assessment: Advanced machine learning models analyze property and borrower data in real-time, providing fair, dynamic interest rates and reducing human bias. - Cross-Chain Liquidity: Lenders can provide capital across multiple blockchain networks, increasing market efficiency and access. - Automated Liquidation & Monitoring: Smart contracts and Chainlink oracles continuously monitor loan health, automatically triggering protective actions to minimize risk for both parties. - Frictionless User Experience: The platform streamlines the entire process—from minting property NFTs to loan origination, funding, and repayment—making real estate lending faster, safer, and more accessible.
How it's made
Challenges we ran into 1. On-Chain & Off-Chain Data Sync Ensuring the frontend always displayed up-to-date property and loan data from the blockchain, especially when metadata was still propagating on IPFS. I Implemented retry logic and fallback mock data in the frontend. Added syncing states to the UI to gracefully handle delays in metadata availability. 2. Smart Contract Integration Bugs I encountered issues with incorrect contract addresses, and BigInt conversion errors (such as passing floating-point APR values). I reviewed the contract ABIs and frontend hooks, updated deployment scripts to ensure all contracts were deployed and addresses were current, and added rounding logic to prevent BigInt errors. 3. AI Risk Assessment API Reliability The AI risk assessment API occasionally failed or returned errors, blocking the loan workflow. I added error handling and fallback logic in the API route to return mock data when the AI service was unavailable, ensuring the frontend workflow was never blocked. 4. Cross-Chain Liquidity Complexity Designing a seamless user experience for cross-chain liquidity provision and loan funding, given the complexity of Chainlink CCIP and multi-chain state. Consolidated cross-chain logic into a single React hook, improved UI feedback, and provided clear user guidance on required steps and network switching.