Web3 technology is increasingly moving beyond experimental applications and into tools designed for broader everyday use. However, many blockchain-based services continue to present challenges for new users because of complicated wallet systems, unfamiliar terminology, multiple network requirements, and difficult interfaces.
Xylo Holdings has introduced Mochi, an AI-powered Web3 asset-management platform designed to reduce those barriers by placing complex blockchain technology in the background and providing users with a more straightforward experience.
Mochi combines artificial intelligence algorithms with an intuitive user interface to support on-chain asset management. The platform is being developed around an all-weather investment approach intended to help manage assets across different market conditions.
Xylo, which develops technology platforms using blockchain and artificial intelligence, said it was preparing an updated version of Mochi centered on a new AI algorithmic engine and a redesigned user experience. The company plans to release a beta version during the second half of the year.
The updated platform is intended to allow users with limited knowledge of Web3 technology to manage on-chain assets through a simplified interface requiring only a few actions.
Simplified Design Targets Wider Web3 Adoption
Xylo has placed significant emphasis on reducing the complexity commonly associated with traditional on-chain services. Many existing platforms require users to connect several wallets, select blockchain networks, and navigate technical dashboards before completing basic tasks.
Mochi is designed to consolidate those processes into a single, more accessible interface. The platform would place its underlying algorithms and blockchain infrastructure behind the user-facing system, allowing users to focus on setting asset-management objectives and reviewing results rather than understanding the technical processes involved.
The company said this approach was intended to lower the entry barriers that have limited the wider adoption of Web3 services. By reducing the number of technical decisions required from users, Xylo aims to make blockchain-based asset management more accessible to people who are unfamiliar with digital wallets, decentralized networks, and other Web3 tools.
All-Weather Strategy Combines Stability With AI Monitoring
Mochi’s technology is built around an all-weather algorithm that distributes assets across growth-oriented, defensive, and hedge-focused categories. The allocation model is designed to reduce the risk of a portfolio becoming overly dependent on a single market direction or investment environment.
An AI engine is expected to operate alongside the allocation framework by monitoring market signals continuously and adjusting asset weights according to predefined risk controls. The company described the all-weather model as the platform’s structural foundation, while the AI system would provide real-time responses to changing market conditions.
The combined framework is designed to balance long-term portfolio stability with dynamic AI-driven adjustments while applying rule-based safeguards during periods of elevated market volatility.
Under the planned system, portfolio allocations could shift toward more defensive configurations when volatility reaches predetermined levels. The company said the rule-based structure was intended to provide additional safeguards rather than relying entirely on automated AI decisions.
Xylo Plans Greater Transparency Through Backtesting Data
Xylo said it intended to disclose the methodology used to evaluate Mochi’s algorithms through historical simulations during the development process. The company plans to present data showing how the system responded across different market environments, including periods of substantial volatility.
Rather than emphasizing performance figures alone, the proposed disclosures would focus on the behavior of the algorithm and the defensive decisions it made under changing market conditions. This approach could allow users to examine how the technology operates before relying on the platform.
Xylo plans to use transparent backtesting methods to demonstrate how Mochi’s algorithms respond to changing market conditions and manage defensive risk.
A company representative indicated that Mochi was being developed around stable technical architecture and an accessible user experience rather than a competition to introduce highly visible features. The representative added that Xylo’s longer-term objective was to expand access to Web3 technology across a broader user base and additional regions.
The company plans to introduce the AI engine and redesigned interface through a series of beta releases. It also expects to open community pre-registration as development progresses.