Problem Statement

• Simple Strategies: DeFi is currently dominated by basic strategies like autocompounding and leveraging. While useful, these strategies don't fully leverage advanced financial engineering or AI, missing out on dynamic market opportunities and comprehensive risk management.

• Fragmented Liquidity: The rise of various chains and rollups has fragmented the DeFi landscape, leading to scattered liquidity. This fragmentation complicates the process for users to maximize returns and creates inefficiencies, especially in implementing cross-chain strategies.

• Opaque Centralization: Despite the ethos of decentralization, DeFi still leans on centralized intermediaries, introducing risks and obscuring vital operational details from users, potentially exposing them to hidden risks.

• On-Chain AI Limitations: Deploying AI on the blockchain is hindered by high computational costs and resource constraints, limiting the sophistication and real-time capabilities of AI-driven financial strategies.

• Demand for Advanced Strategies: The evolving DeFi market demands complex strategies that can seamlessly navigate and optimize across diverse protocols and environments, a demand that current protocols don't sufficiently meet due to their lack of adaptability and depth.

• Inconsistency in AI Outputs: AI's potential in DeFi is undeniable, yet its early-stage models can produce unreliable outputs. Ensuring the effectiveness of AI in DeFi requires robust validation, error handling, and result interpretation frameworks.