Roadmap¶
DeepTrust Protocol is being developed in phases, with each milestone focused on enhancing the security, scalability, and real-world applicability of decentralized AI governance. Below is our roadmap, outlining the key development phases.
🚀 Q1: Mainnet Launch & Initial AI Node Deployment¶
The first major milestone is the mainnet launch, where DeepTrust transitions from development and testnet phases into full production. This phase includes: - Deployment of AI nodes, ensuring that validators can securely process AI queries. - Implementation of cryptographic proofs, enabling zero-knowledge verification of AI outputs. - Integration with Arbitrum Orbit L3, optimizing AI query execution and transaction costs.
This phase establishes the trust layer for AI verification, allowing decentralized AI services to begin onboarding users and proving their legitimacy.
🔍 Q2: Expanding Validator Network & Early Adoption¶
With the mainnet running, the next step is expanding the validator network to ensure decentralized AI verification. In this phase, we focus on: - Onboarding independent validators, who confirm AI interactions using Merkle trees and session hashes. - Launching the DeepTrust reputation system, allowing AI nodes to be ranked based on accuracy and reliability. - Expanding partnerships with decentralized applications (dApps) and enterprises that require trustworthy AI services.
By the end of Q2, DeepTrust will have a robust network of validators ensuring that AI models remain trustworthy and censorship-resistant.
📈 Q3: Multi-Chain Expansion & AI Service Marketplace¶
To support cross-chain AI verification, DeepTrust will expand to additional blockchains, ensuring that AI services can be used across multiple ecosystems. Key initiatives include: - Multi-chain interoperability, allowing AI verification to work across Ethereum, Solana, and other networks. - Launching an AI service marketplace, where developers can deploy and monetize AI-powered dApps. - Enhancing System Governance, enabling governments, enterprises, and DAOs to build custom governance frameworks on top of DeepTrust.
This phase positions DeepTrust as the foundational layer for verifiable AI services in Web3.
🌍 Q4: Real-World Adoption & Compliance Frameworks¶
The final phase of the initial roadmap focuses on enterprise and regulatory adoption, ensuring that DeepTrust can serve governmental, corporate, and institutional AI applications. Key developments include: - Building compliance frameworks, allowing AI nodes to be certified by regulatory bodies. - AI governance for real-world applications, such as healthcare, finance, and public sector services. - Expanding validator participation, ensuring that AI verification remains decentralized and resilient to manipulation.
By the end of this phase, DeepTrust aims to be the go-to platform for trust in decentralized AI.
📌 Summary¶
The DeepTrust roadmap is structured to ensure gradual, scalable, and impactful growth. From securing AI models to expanding governance frameworks, each phase builds towards a trust-based AI economy.
🌟 Next Step: Explore real-world Use Cases.