In the industrial cycle of deep integration of Web3 and AI, the data ecosystem consistently faces three structural contradictions: the lack of protocols for cross-chain data collaboration (fragmentation of multi-chain data, lack of unified interaction standards), the interface gap between AI and data (raw on-chain data is difficult to meet the feature engineering needs of AI models), and flaws in the data value distribution mechanism (contributors only obtain one-time call benefits, disconnecting from long-term appreciation of the ecosystem). Chainbase does not limit itself to a shallow positioning of 'data tools', but constructs a three-layer system of 'protocol-based data collaboration - AI-native adaptation - programmable value distribution' with Hyperdata Network as the technical core, becoming one of the few hub projects in the DataFi track with the capability for underlying protocol innovation, its core competitiveness lies in restructuring the full-link logic of Web3 data from 'collection - processing - application - distribution'.
I. Technical core: The protocol-based data collaboration architecture of Hyperdata Network
Chainbase's core breakthrough lies in establishing a foundational protocol for cross-chain data collaboration through Hyperdata Network, rather than simply aggregating multi-chain data. The essence of this architecture is to define the standards for 'how data flows between multiple chains and how it is called by AI', addressing the protocol void in Web3 data collaboration, with all technical details based on publicly available project technical documents and testnet data:
1. Modular data collection protocol: Breaking the 'protocol island' of cross-chain data
Hyperdata adopts a dual-layer collection architecture of 'dynamic node network + lightweight adaptation protocol', achieving standardized data access for over 200 chains (including Ethereum, BNB Chain, Sui, Base, etc.):
• Dynamic node network: Deploying high-performance verification nodes for mainstream public chains (such as Ethereum, Base) to ensure real-time (cross-chain data delay ≤ 100ms) and reliability (data verification accuracy ≥ 99.9%) based on a PoS+DPoS hybrid mechanism; nodes gain data verification rights by staking $C, and malicious nodes will face staking penalties, creating a decentralized trust foundation.
• Lightweight adaptation protocol (LAP): Designed for small and medium public chains and Layer 2 (like Scroll, Aptos), creating low-access-cost adaptation modules that allow for data interface standardization without requiring public chains to modify underlying code, compressing the new connection cycle from the traditional 1 month to 7 days, significantly lowering the protocol threshold for cross-chain data collaboration.
As of Q4 2024, Hyperdata has cumulatively processed over 500 billion cross-chain data calls, supporting on-chain data types covering transaction trails, contract status, asset holdings, event logs, and 12 core dimensions, and has achieved semantic alignment of cross-chain data through the 'Homogeneous Data Association Protocol (HDAP)' - for example, the asset of the same user on different chains can be mapped to a unified 'cross-chain identity asset view' through HDAP, providing complete user profile data for AI models.
2. AI-native data processing protocol: Filling the 'interface gap' between data and AI
Hyperdata's core differentiation lies in its built-in 'AI feature engineering protocol (AFEP)', which directly converts raw on-chain data into structured features that can be called by AI models, addressing the limitation of traditional data projects where 'data output is the endpoint':
• Automated feature extraction: Based on pre-trained Transformer models, it automatically identifies high-value features in the data (such as user behavior sequences, asset volatility coefficients, contract interaction risk factors), generating feature vectors that comply with mainstream frameworks like TensorFlow and PyTorch, enabling AI models to avoid additional data cleaning and feature engineering, improving development efficiency by 400%.
• Dynamic feature iteration mechanism: Real-time optimization of feature dimensions through 'AI feedback loop' - After the AI model calls the data, Hyperdata analyzes the model's prediction accuracy and feature importance weight, automatically adjusting the feature extraction logic (such as adding dimensions like 'cross-chain asset flow frequency' and 'address security rating'), achieving co-evolution of data and AI models.
To further improve the supply of AI data, Hyperdata has reached a deep protocol-level cooperation with Chainlink Scale, accessing institutional-level data sources such as macroeconomic indicators, asset security scores, and off-chain compliance data, achieving standardized integration of multi-source data through the 'On-chain + Off-chain Data Fusion Protocol (ODFP)', enabling AI models to directly call composite features of 'on-chain transaction data + off-chain credit scores', significantly enhancing the decision-making accuracy of models (such as a 15%-20% improvement in the default rate prediction accuracy of DeFi risk control models).
II. Ecological collaboration: From tools to protocols developer empowerment system
Chainbase's ecological competitiveness lies in outputting Hyperdata's protocol capabilities through standardized tools, lowering the technical threshold for developers, while expanding the application boundaries of data collaboration through protocol-level cooperation with leading ecosystems, all ecological data is sourced from publicly available project ecological reports and on-chain data:
1. Manuscript developer suite: The 'popular entry' for protocol capabilities
To enable developers to call its capabilities without understanding the underlying protocol details of Hyperdata, Chainbase has launched the Manuscript suite (including GUI visualization tools and CLI command-line tools), the core is to encapsulate protocol logic as a 'low-code interface':
• Cross-chain data call protocol encapsulation: Developers can generate cross-chain data call logic through GUI drag-and-drop, with the tool automatically converting it into code compliant with Hyperdata protocol (supporting multiple languages such as Solidity, Move, and Rust), for instance, calling 'cross-chain asset holding data' only requires 3 steps without the need to manually write cross-chain interaction code.
• AI feature template library: Built-in feature templates for 10 categories of high-frequency AI scenarios such as DeFi risk control, NFT valuation, and market trend prediction, allowing developers to directly call predefined feature engineering protocols without building a feature system from scratch. For instance, when developing an NFT AI valuation tool, one can directly use the combination feature template of 'historical transaction premium rate + creator influence'.
As of Q4 2024, Manuscript has served over 20,000 developers, 40% of whom are focused on AI-driven Web3 application development; projects developed through Manuscript have exceeded 8,000, covering four major areas: DeFi (35%), NFT (28%), AI tools (22%), and infrastructure (15%), forming a developer ecosystem centered around the Hyperdata protocol.
2. Protocol-level cooperation with leading ecosystems: Expanding the industrial boundaries of data collaboration
Chainbase integrates Hyperdata's collaborative capabilities into core industrial scenarios through protocol-level integration with leading ecosystems like Base, Coinbase, and Sui, rather than superficial functional connections:
• Deep integration with Base's OP Stack: As the officially recommended data protocol for Base chain, Hyperdata's cross-chain data interface has been integrated into Base's OP Stack foundation, allowing developers within the Base ecosystem to directly call Hyperdata's cross-chain data via OP Stack without additional deployment of adaptation modules; currently, 60% of AI projects within the Base ecosystem (such as cross-chain lending risk control and on-chain behavior analysis tools) use Hyperdata's protocol services, with data call frequency accounting for 28% of the total data demand within the Base ecosystem.
• Protocol-level access to Coinbase CDP wallet: As one of the first data protocol partners for Coinbase's embedded wallet (CDP), Hyperdata's user data collaboration protocol has been integrated into Coinbase's user system, which will provide 110 million Coinbase users with a closed-loop experience of 'on-chain data - AI services' in the future (such as AI financial advice based on user on-chain behavior), achieving penetration of data collaboration from B-end to C-end.
III. Value mechanism: The programmable value distribution logic of $C token
The value loop of Chainbase lies in making the full-link value of data collaboration programmable through the C token, so that C is not only a trading target but also the core carrier of data value distribution, all token mechanisms are based on the project white paper and smart contract audit reports:
1. Token economy design: Deeply tied to data collaboration value
$C's total supply is 1 billion tokens, with TGE completed in July 2025, and its distribution mechanism strictly revolves around the 'data collaboration ecosystem' to prevent excessive capital from occupying ecosystem profits:
• Ecological incentives (65%): 40% for community and integrated projects (developers can obtain C rewards by integrating Hyperdata protocol), 12% for data verification nodes (nodes earn C by ensuring the real-time and accuracy of data collaboration), 13% for airdrops (to attract users to participate in data collaboration testing and ecological construction);
• Long-term development (35%): 17% for early investors (3-year linear unlock, locked for the first 12 months), 15% for core team (3-year linear unlock), 3% for initial liquidity support.
2. Programmable value distribution: Dynamic profit sharing based on smart contracts
$C's core innovation lies in 'programmable distribution of data value', dynamically linking revenue to data collaboration contributions through smart contracts:
• Dynamic adjustment of node rewards: The $C rewards for data nodes are not only linked to the amount of staking but are also positively correlated with three dimensions: 'the frequency of data being called by AI', 'the cross-chain scope of data collaboration', and 'feedback on the effectiveness of the AI model' - for example, if the data of a certain node is called by more than 10 AI models and it boosts the model's accuracy by 10%, its reward coefficient will increase to 2.5 times the base value;
• Fee destruction and increased scarcity: 5% of API call fees (paid in C) will be permanently destroyed, as the scale of data collaboration expands (API call volume is expected to exceed 1 trillion by 2026), the circulation of C will gradually decrease, and scarcity will continue to increase;
• Market performance and valuation logic: $C has been listed on leading exchanges such as Binance, MEXC, and Bithumb, with Binance's C/USDT trading pair maintaining a 24-hour trading volume of over $47 million, accounting for 60% of total trading volume, indicating ample liquidity; the current price range is $0.2130-$0.2925, fully diluted valuation (FDV) is $187 million-$282 million, compared to similar data projects (such as The Graph FDV approximately $1.2 billion), its valuation remains within a reasonable range and has premium space for synergy with the AI ecosystem.
IV. Future evolution: From data hub to Web3+AI infrastructure standard
Based on Chainbase's public roadmap and the industrial trend of Web3+AI, its future development will focus on three main directions, with the core being the upgrade from 'data collaboration hub' to 'Web3+AI data protocol standard setter':
1. Expansion of global data protocols: From multi-chain to cross-domain
In the next 1-2 years, Hyperdata will integrate data sources from vertical fields such as the Internet of Things (IoT), supply chain, and government compliance, achieving full-domain data collaboration through the 'Cross-domain Data Adaptation Protocol (CDAP)' for 'on-chain + off-chain + vertical industry'; simultaneously, it will introduce ZKML (zero-knowledge machine learning) technology to develop a 'Privacy Protection Data Collaboration Protocol (PPDP)' to address data privacy issues in sensitive areas like healthcare and finance - for instance, on-chain medical data from hospitals can be trained with AI models via ZK proof without exposing raw data, meeting compliance requirements. It is expected that by 2026, the supported types of data sources will exceed 50, with AI-ready datasets increasing by 300% compared to the current size.
2. Activation of C-end data collaboration: From B-end to B+C
Leveraging the user base of Coinbase CDP wallet, Chainbase will launch the 'Personal Data Collaboration Protocol (PDCP)', allowing users to authorize on-chain behavioral data (such as transactions, holdings, social interactions) which can be transformed into AI-usable features via Hyperdata and accessed for C-end services such as AI financial management and personalized NFT recommendations; users can earn $C profit sharing from data calls through smart contracts and can independently set the scope of data authorization (e.g., only open 'historical trading trends', not open specific transaction amounts), achieving 'data sovereignty belongs to users, data value belongs to users'. It is expected that by 2026, C-end users will exceed 10 million, forming a data collaboration closed loop between B-end (enterprise applications) and C-end (personal services).
3. Establishing industry standards: From project practice to protocol specifications
In the long term, Chainbase will collaborate with leading AI companies (such as OpenAI, Anthropic) and blockchain projects (such as Ethereum Foundation, Base) to publish (Web3+AI data collaboration protocol specifications), defining industry standards for data collection, feature engineering, privacy protection, and value distribution; its Hyperdata architecture is set to become a reference template for Web3 data collaboration, pushing DataFi from 'tool competition' to a new stage of 'protocol competition'. It is expected that by 2027, the volume of data collaboration calls processed by Hyperdata will exceed 2 trillion times, serving over 1 billion users, becoming the world's largest decentralized Web3+AI data protocol platform.
Summary: The protocol-level value and industrial positioning of Chainbase
Chainbase's core competitiveness lies not in 'multi-chain data aggregation' or 'AI data tools', but in establishing a foundational protocol for Web3 data collaboration through Hyperdata Network, addressing three structural contradictions: the lack of protocols for cross-chain data, the interface gap between AI and data, and the flaws in the value distribution mechanism. Its differentiated advantage is reflected in:
1. Technical level: The protocol architecture of Hyperdata defines the standards for data collaboration, rather than simply implementing functions;
2. Ecological level: The collaboration between Manuscript tools and leading ecosystems at the protocol level has lowered the usage threshold for developers, expanding the application boundaries of the protocol;
3. Value aspect: The programmable allocation mechanism of $C allows data contributors to share the long-term appreciation of the ecosystem, rather than just obtaining a one-time benefit.
Although $C is currently in a price correction cycle (about 55% correction from ATH), considering the industrial growth of Web3+AI (expected to exceed $10 billion in market size by 2025), Chainbase's first-mover advantage in the data protocol field, and a reasonable FDV of $187 million-$282 million, its long-term value still possesses significant growth potential. For the industry, Chainbase is the infrastructure for Web3+AI data collaboration; for investors, its protocol-level innovation capability makes it one of the few quality targets with a long-term moat in the DataFi track.
