MBD — is a decentralized artificial intelligence protocol designed for personalization and content moderation in the Web3 space. It combines blockchain and external data to generate dynamic recommendations and social feeds with minimal latency. The project provides developers with ready-to-use tools to integrate custom feeds, search and content filtering, as well as an API for building scalable solutions. Thanks to collaborations with Coinbase Wallet and Base, MBD has already proven the effectiveness of its approach, delivering a more relevant and convenient user experience with digital services.
- Architecture and key capabilities of MBD
- Integrations and protocol use cases
- MBD tech stack and API for developers
- MBD: development strategy and business model
- Conclusion
Architecture and key capabilities of MBD
The MBD protocol is designed as a universal personalization layer for Web3 applications, combining machine learning with blockchain activity analysis. The architecture is built around two primary data streams: on-chain and off-chain. The first covers interactions in Base, Ethereum, and Farcaster, capturing social ties and transactions. The second works with external sources — media files, social graphs, and user preferences — providing a complete picture of behavior. This fusion enables dynamic feeds and real-time adaptation of recommendations with latency under 150 ms.
The architecture is underpinned by several core components:
- Recommendation ML models trained on hundreds of thousands of hours of data;
- Feed Builder for no-code feed configuration;
- API and SDK to streamline integration into wallets and dApps;
- Moderation system that filters spam and irrelevant content.
Together these elements form a unified mechanism that not only increases recommendation accuracy but also ensures stability under high load. The stack is complemented by flexible filters: users can sort materials by authors, topics, or content types, while developers can customize algorithms for specific scenarios. This approach turns MBD into a foundational platform capable of serving millions of requests while remaining accessible to startups and large ecosystems alike.
Integrations and protocol use cases
The adoption of the MBD protocol shows how AI algorithms can enhance social and transactional functions in the Web3 environment. The most illustrative cases involve integrations into wallets and social platforms, where responsiveness and personalization are critical. Together with Base, a Discover section was implemented, allowing users to browse content through dynamic feeds reminiscent of traditional social networks. Recommendations are formed based on on-chain activity and preferences and also take the social graph into account.
Main integrations can be summarized in the table:
| Integration | Description |
|---|---|
| Base App | Personalized Discover section with media content and a social feed. |
| Coinbase Wallet | Beta version of the wallet with smart recommendations and in-feed transactions. |
| AI Agents (Zo) | Semantic search and personalized responses based on on-chain history. |
These implementations show that the MBD protocol is not limited to being a technical add-on but becomes a strategic tool for ecosystem growth. Integrations with major players confirm its scalability, while AI agents open up new usage scenarios — from personal assistants to recommendation services. This positions the project as an emerging standard of personalization and moderation across decentralized social networks and cryptocurrency wallets.
MBD Tech Stack and API for Developers
The technical infrastructure of MBD is based on scalable ML models and integration tools. The system processes millions of requests with a response time of under 150 milliseconds, which is critical for social applications and wallets. The models have been trained for over one hundred thousand hours and demonstrate a multiple increase in recommendation accuracy compared to traditional algorithms.
For developers, a REST API is provided that enables embedding recommendations and moderation into any application, while SDKs for TypeScript and React simplify adoption within Web3 ecosystems. Additionally, Feed Builder is available, allowing the creation of custom feeds without coding. This makes the technology applicable to both large projects and smaller teams.
Flexible configuration allows consideration of social connections, thematic interests, and user behavior. Developers can define their own filtering and ranking algorithms, turning MBD into an adaptive platform. This approach combines speed and accuracy with ease of integration, ensuring users receive personalized access to content. Furthermore, the project is designed with scalability in mind, maintaining stable performance as the number of connected applications grows.
MBD: Development Strategy and Business Model
The financing and development model of MBD highlight the project’s ambition to become a leading provider of AI solutions for Web3. At an early stage, the company raised $3 million in a pre-seed round from major funds and strategic partners. The funds are directed toward team expansion and product development, creating a strong foundation for further growth. The business model revolves around providing API and SDK access for wallets, social platforms, and AI agents, where personalization and moderation are essential.
The project’s strategic priorities can be outlined as follows:
- Expanding partnerships with major ecosystems, including Coinbase Wallet and Base;
- Supporting startups through accelerator programs with AI credits;
- Strengthening the research team to improve ML models.
These steps shape a multi-layered strategy where monetization is driven by API subscriptions, paid developer tools, and custom integrations for enterprise clients. This approach allows the project to serve both the mass market and niche initiatives. In the long term, MBD aims to become the intelligence layer of Web3, merging data and algorithms, thereby solidifying its position as the standard for personalized interaction in decentralized environments.
Conclusion
MBD is shaping a new standard of personalization in Web3 by combining blockchain and external data to create intelligent feeds, moderation tools, and flexible APIs. Successful integrations with Coinbase Wallet and Base confirm the model’s viability and the demand for such solutions among major players. A well-thought-out development strategy, a strong team, and venture capital backing enable the project to establish itself as a core layer for decentralized social applications. The future of MBD is tied to expanding partnerships and further integration into the Web3 ecosystem. Altogether, the project stands out as one of the most significant directions in the evolution of next-generation social interaction.
