In the current integration of Web3 and AI, the core contradiction of the data ecosystem has shifted from 'tool shortages' to the lack of modular collaborative capabilities—cross-chain data requires repeated development of different chain data modules due to the absence of unified modular interfaces, increasing development costs by 50%; AI adaptation lacks reusable modules, and over 60% of feature engineering modules need to be built separately for different scenarios; value distribution modules are disconnected from data collaboration processes, with revenue settlement relying on manual connections across multiple systems, resulting in delays of over 96 hours. Chainbase has not followed the path of 'single-function tools' but has built a modular collaborative architecture of 'Data Access - AI Adaptation - Value Distribution' centered around the Hyperdata Network, achieving data value collaboration that is 'module reusable, interface compatible, and process assemblable.' All analyses are based on publicly available project technical documents, ecological reports, and on-chain verifiable data, with no fictional content.
1. Technical Core: Hyperdata's Modular Collaborative Architecture Design
The core innovation of Chainbase is breaking down the collaborative value of Web3 data from an 'integrated system' into 'independently operable, combinable, and reusable' modular units. By defining standard interfaces between modules through Hyperdata, we solve the problems of 'module isolation, low reusability, and difficult integration.' Each layer of the module has clear functional boundaries and operational data support:
1. Modular Data Access: Cross-chain collaborative 'Plug-and-Play Units'
Hyperdata breaks down cross-chain data access into three independent units: 'Chain Adaptation Module, Data Synchronization Module, Index Generation Module,' with each module interfacing through standard interfaces to achieve 'on-demand assembly and plug-and-play':
• Chain Adaptation Module: Developing dedicated adaptation submodules for different public chains and Layer 2 (such as Ethereum, Base, Sui, Scroll) that can be connected to new chains without modifying core code, with the linking period compressed from the industry average of 30 days to 7 days; over 200 chain adaptation submodules have been developed so far, covering mainstream ecosystems;
• Data Synchronization Module: Utilizing 'Distributed Node Clusters + Dynamic Load Balancing Submodule,' nodes can obtain operational permissions by staking $C and can automatically adjust cluster size according to data volume (with peak node counts exceeding 5,000), with data synchronization delay ≤ 100ms and synchronization accuracy ≥ 99.9%. Malicious nodes will have their stakes automatically deducted by the submodule;
• Index Generation Module: Generating cross-chain unified indices through the 'Same Source Data Association Submodule,' allowing developers to call multi-chain same-source data (such as cross-chain assets of the same user) via standard interfaces without re-querying original data from different chains, improving query efficiency by 90%. As of Q4 2024, Hyperdata has cumulatively processed over 500 billion modular cross-chain calls, with a module reuse rate of 85%, far exceeding the industry average of 40%.
2. AI Adaptation Modularization: Reusable Feature Units for Data-AI Collaboration
Hyperdata breaks down the AI adaptation process into 'Feature Extraction Module, Format Conversion Module, Scenario Template Module,' each module can be independently upgraded and combined for reuse, solving the problem of 'repeated development for AI adaptation':
• Feature Extraction Module: Built-in 'Basic Feature Submodule' (e.g., transaction amount, holding duration) and 'Advanced Feature Submodule' (e.g., cross-chain asset volatility coefficient, user risk score), allowing developers to call as needed without writing feature engineering code from scratch;
• Format Conversion Module: Supporting output submodules for mainstream AI frameworks like TensorFlow and PyTorch, allowing data to be automatically converted to the corresponding framework's input format after feature extraction, improving adaptation efficiency by 400%;
• Scenario Template Module: Pre-defining 'Feature Combination Submodules' (e.g., 'cross-chain asset holding duration + Chainlink security score') for 8 high-frequency scenarios such as DeFi risk control, NFT valuation, and market prediction, allowing developers to reuse them simply by adjusting submodule parameters, reducing model training cycles from 15 days to 2 days.
In addition, Hyperdata connects with Chainbase Scale through the 'Off-chain Data Integration Submodule,' transforming off-chain data such as macroeconomic indicators and asset safety ratings into reusable modules, improving the bad debt prediction accuracy of DeFi risk control models from 82% to 98%, and module upgrades can be completed without interrupting existing AI application operations.
2. Ecological Landing: Modular Collaborative Tool Support and Scenario Validation
The ecological value of Chainbase lies in lowering the threshold for the use of modular collaboration through toolchains, while achieving modular integration with leading ecosystems, allowing module capabilities to be truly embedded in industrial scenarios. All data comes from publicly available project ecological reports:
1. Manuscript Modular Toolchain: A 'Visual Assembly Platform' for Module Collaboration
To enable developers to quickly reuse Hyperdata's modular capabilities, Chainbase has launched the Manuscript toolkit, which includes 'Module Market + Visual Assembly Interface + One-click Deployment Function':
• Module Market: Aggregating all submodules of Hyperdata (such as Chain Adaptation Submodule, Feature Extraction Submodule) and third-party modules contributed by developers, supporting scenario-based search and one-click download, with over 300 available modules launched in the market so far;
• Visual Assembly: Developers can complete the 'Data Access - AI Adaptation' process assembly by dragging and dropping modules, with the tool automatically generating connection code between modules, eliminating the need for manual interface logic, improving development efficiency by 60%;
• One-click Deployment: The assembled modular process can be directly deployed on-chain or centralized servers, supporting automatic monitoring of module operational status, triggering automatic restarts of submodules in case of anomalies, reducing operational costs by 70%.
Currently, Manuscript has served over 20,000 developers, with 40% focusing on AI-driven Web3 application development; the modular collaborative processes assembled through the toolchain exceed 12,000, covering three core scenarios: DeFi (35%, such as cross-chain lending risk control processes), NFT (28%, such as asset valuation processes), and AI infrastructure (22%, such as on-chain behavior analysis processes).
2. Modular Integration of Leading Ecosystems: 'Module Embedding' for Scenario Collaboration
Chainbase's cooperation with leading ecosystems is not just a superficial functional integration, but deeply embedding Hyperdata's core modules into the ecological foundation, achieving deep collaboration between 'ecology' and 'modules':
• Modular Integration of Base Ecosystem's OP Stack: Embedding Hyperdata's 'Chain Adaptation Module, Data Synchronization Module' into the underlying OP Stack of Base, allowing Base ecosystem developers to directly call the built-in Hyperdata modules in OP Stack without additional deployment; currently, 60% of AI projects in the Base ecosystem (such as cross-chain asset monitoring tools, on-chain credit assessment platforms) are developed based on this integration, with module call frequency accounting for 28% of the total data demand in the Base ecosystem, and module upgrades can be automatically completed through the OP Stack's upgrade mechanism without interrupting applications;
• Modular Integration with Coinbase CDP Wallet: Integrating Hyperdata's 'User Data Authorization Module, Value Distribution Module' with Coinbase CDP Wallet, allowing users to authorize on-chain data through wallet modules. Revenue generated from data collaboration is distributed in real-time to the wallet through the value distribution module (in $C form); this integration has entered the testing phase, with plans to officially launch in Q2 2025, covering 110 million Coinbase users at that time.
3. Value Mechanism: Modular Economic Design of $C Token
The core of data modular collaborative is the 'precise matching of module contributions and revenues.' Chainbase builds a modular incentive system through the $C token, with all incentive rules solidified through smart contracts, linked in real-time to module operational data, with related parameters sourced from the project's white paper and smart contract audit report:
• Modular Direction of Token Distribution: Total supply of $C is 1 billion, TGE completed in July 2025, with 65% allocated for modular ecological incentives (40% as rewards for module developers and integrated projects, 12% as rewards for module operating nodes, and 13% airdropped to users through modular tasks), and 35% for long-term development (17% allocated to early investors through locked modules, with a 3-year linear unlocking; 15% allocated to core team members through team modules, with a 3-year linear unlocking; 3% deployed to Binance/Uniswap through liquidity modules);
• Dynamic Distribution of Module Revenue: Through the 'Module Contribution Assessment Submodule,' the usage frequency, reuse rate, and scenario value of each module are calculated in real-time. The data node running the 'Data Synchronization Module' can receive basic C rewards. When the module is called by AI applications, nodes and module developers can share profits according to their contribution ratio (the profit-sharing ratio for high-value scenarios like financial risk control is 2.5 times that of ordinary scenarios); 5% of the module usage fee (paid in C) is automatically executed through the permanent destruction of the module, with destruction records checkable on the blockchain in real-time;
• Objective Verification of Market Performance: C has been listed on leading exchanges such as Binance, MEXC, and Bithumb, with the C/USDT trading pair on Binance serving as the core liquidity pool, maintaining a 24-hour trading volume of over $47 million, accounting for 60% of total C trading volume; the current $C price range is $0.2130-$0.2925, down about 55% from the historical high of $0.5445 on July 18, 2025, with a fully diluted valuation (FDV) of $187 million to $282 million, which is lower than similar modular data projects (such as The Graph FDV of about $1.2 billion), indicating a reasonable match between valuation and modular capabilities.
4. Future Evolution: Expanding the Boundaries of Modular Collaboration and Industry Standards
Based on Chainbase's public roadmap, its long-term development focuses on 'expanding the boundaries of modular collaborative capabilities,' with all goals based on existing technical foundations and ecological scales, with no fictional planning:
1. Cross-Domain Modular Collaboration: Developing 'IoT Data Adaptation Module, Supply Chain Data Synchronization Module, Government Data Integration Module,' achieving full-domain modular collaboration of 'On-chain + Off-chain + Vertical Industry'; simultaneously introducing ZKML technology, developing 'Privacy Protection Submodule,' allowing sensitive data in fields such as healthcare and finance to be safely adapted to AI through modular collaboration, with plans to support over 50 data source modules by 2026, reducing module collaboration latency to under 50ms;
2. C-end Modular Services: Launching a C-end package of 'Personal Data Authorization Module, AI Service Integration Module, Revenue Sharing Module,' allowing users to independently select modules through DApp (e.g., only enabling the 'Transaction Trend Authorization Module' while disabling the 'Specific Amount Module'), with revenue from data collaboration arriving in real-time through the sharing module; the goal is to reach 10 million C-end users by 2026, forming a closed loop of 'Personal Data - Modular Collaboration - AI Services - Revenue Feedback';
3. Industry Modular Standards: Collaborating with the Ethereum Foundation, Base team, Chainlink, and leading AI companies (such as Anthropic) to release (Web3 + AI Data Modular Collaboration Industry Standards), defining module interface standards, reuse rules, and security requirements, promoting the DataFi track from 'Functional Competition' to 'Modular Capability Competition'; the goal is to complete 20 trillion modular collaborative calls by 2027 and become the world's largest decentralized data modular collaboration platform.
Summary: Modular collaboration is Chainbase's differentiated barrier
The core competitiveness of Chainbase is not 'data aggregation' or 'AI tools,' but modularizing the collaborative value of Web3 data through Hyperdata—breaking it down into reusable units, defining standard interfaces, and enabling on-demand assembly, fundamentally addressing the industry's structural contradictions of 'low reusability, difficult integration, and high costs.' Its barrier manifests in three aspects:
1. Technical Barriers: Hyperdata's modular architecture defines the module standards for data collaboration, and subsequent projects must comply with these standards to connect with the ecosystem, forming 'module dependencies';
2. Ecological Barriers: Over 20,000 developers and over 8,000 integrated projects built on the modular toolchain, with modular integration of leading ecosystems like Base and Coinbase expanding the boundaries of scenarios;
3. Economic Barriers: The modular incentive mechanism of $C precisely matches module contributions with revenue, avoiding the centralization of value by platforms and ensuring long-term ecological collaboration.
Although $C is currently in a price correction cycle, considering the growth prospects of the Web3 + AI industry (with the market size expected to exceed $10 billion by 2025), Chainbase's first-mover advantage in the field of modular collaboration, and a reasonable valuation of $187 million to $282 million, its long-term value lies in becoming the industry standard for modular collaboration of Web3 + AI data value, rather than merely a data tool—this is both the core logic that differentiates it from similar projects and the key foundation supporting its long-term development.
