Arrakis has announced the launch of Arrakis Pro, a newly enhanced market-making solution designed to optimize on-chain liquidity for token issuers. With a modular framework, Arrakis Pro allows projects to deploy liquidity strategies on any AMM, any decentralized exchange, and across chains.

Arrakis Pro is the first on-chain native market maker, now revamped to support token issuers.
The solution tackles liquidity depth, capital efficiency, and MEV protection.
Arrakis Pro’s modularity enables liquidity deployment on diverse protocols and platforms.

Arrakis Pro, previously known as PALM, has built a reputation as DeFi’s go-to solution for liquidity management. By automating liquidity provision and rebalancing in concentrated liquidity pools, Arrakis Pro enables teams to achieve deep on-chain markets efficiently. This efficiency allows token issuers to avoid issues like out-of-range liquidity or excessive volatility, which can lead to lost fees and high slippage.

The updated framework responds to changing trends in the AMM space, particularly as new designs like Uniswap V4’s Hooks introduce greater customization. With modularity at its core, it provides token issuers the flexibility to manage liquidity and deploy market-making strategies on next-generation platforms, addressing liquidity fragmentation as the AMM space grows.

The solution also addresses the “Inventory Problem” in DeFi, where token issuers often lack sufficient base assets, like USDC or ETH, to achieve liquidity depth. By rebalancing token inventories over time without external capital, it enables issuers to build liquidity efficiently. Active liquidity management algorithms help maintain price ranges, while MEV protection features minimize arbitrage-related losses.

As an established solution, Arrakis has managed over $500 million in trading volume, offering liquidity management that supports both emerging projects and established players like Lido and Stargate. According to their team, Arrakis Pro aims to redefine liquidity management, with a modular approach ready to scale as the AMM landscape evolves.

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