We’re excited to be a part of Based India because we need smarter, safer tools for liquidity providers and traders alike. Which is why we created SmartSwap. By using Base's infrastructure, we are able to build highly innovative and intelligent Automated Market Maker (AMM) solution, SmartSwap empowers liquidity providers and traders both. This hackathon provides a great platform to contribute to the DeFi ecosystem in India and explore the technical advancements that Base enables for cross-chain solutions.

One of the biggest challenges in DeFi, is dealing with losses during periods of high market volatility. We are addressing the challenge of liquidity provider (LP) losses during periods of elevated volatility. Traditional AMMs are static in nature and do not adapt to fluctuating market conditions, often leading to significant losses for LPs due to arbitrage and impermanent loss. These issues are particularly acute in volatile markets, where LPs face Loss-Versus-Rebalancing (LVR) risk. This problem is not only common globally but also limits the appeal of DeFi in emerging markets like India, where users may need to be more aware of liquidity risks in decentralized exchanges.

SmartSwap introduces AI/ML-powered dynamic fee mechanisms that adjust trading fees in real-time based on market volatility. This reduces LP exposure to arbitrageurs and LVR, protecting their assets during volatile market conditions. Our solution leverages Base's infrastructure to facilitate cross-chain communications, enabling a broader scope for liquidity management across EVM-compatible chains. With on-chain AI/ML inference on Base, SmartSwap can respond intelligently to market changes, providing liquidity providers with better risk management tools and a safer trading environment.Through SmartSwap, we aim to make decentralized finance safer for all participants, encouraging more users to become liquidity providers and helping DeFi ecosystems thrive.

One of the key technical challenges was ensuring seamless on-chain AI/ML inference, as running machine learning models directly on a blockchain requires significant optimization. We tackled this by using Base. Additionally, implementing cross-chain communication using CCIP to enable our volatility oracle to service other chains was a non-trivial task. We overcame these challenges by rigorously testing our cross-chain communication protocols and optimizing the model deployment and inference processes. Another hurdle was ensuring fair simulations to benchmark our dynamic fee model against traditional AMMs, which required developing a simulation program that accounts for market conditions and rational trading strategies.