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About

The problem it solves 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.

How it's made

Challenges we ran into 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.