The core contradiction between Web3 and AI is not the insufficient total amount of data, but rather the static nature of the data that makes value transformation difficult - due to the lack of a unified transformation standard, only 30% of cross-chain data can be effectively reused; original on-chain data requires over 60% offline processing to adapt to AI, resulting in extremely low transformation efficiency; the distribution of benefits after data value transformation is delayed, with contributors only able to obtain less than 15% of the value-added benefits. Chainbase does not follow the shallow route of 'data tools', but instead builds a dynamic system centered around Hyperdata Network to achieve 'cross-chain transformation - AI adaptation transformation - value distribution transformation,' transforming data from 'static assets' into 'liquid, adaptable, profit-sharing' dynamic value carriers. All discussions are based on publicly available project technical documents, ecological reports, and on-chain verifiable data.
One, technical core: Hyperdata's dynamic transformation architecture
Chainbase's core innovation is defining the standards for how Web3 data 'cross-chain transforms, adapts to AI transformation, and shares profits' through Hyperdata, rather than simply aggregating data. The essence of this architecture is the 'data value dynamic transformation protocol stack,' with each layer corresponding to clear technical specifications and operational data:
1. Cross-chain transformation: Breaking the 'static islands'
Hyperdata achieves standardized cross-chain transformation of data from over 200 chains (including Ethereum, BNB Chain, Sui, Base, etc.) through a 'dynamic node network + lightweight transformation protocol (LAP),' solving the problem of 'difficulty in cross-chain data flow':
• Dynamic node network: Using a PoS staking + DPoS consensus mechanism, nodes need to stake C to obtain data transformation permissions, completing the transformation process of 'on-chain data format alignment - semantic unification - index generation' in real-time through distributed nodes, with cross-chain data transformation delay ≤100ms, improving by 80% compared to traditional data projects (with delays generally over 500ms); data transformation accuracy ≥99.9%, malicious nodes will have their staked C confiscated and be blacklisted, ensuring a decentralized trust foundation for transformation.
• Lightweight transformation protocol: For medium and small chains and Layer 2 such as Scroll and Aptos, data transformation adaptation can be completed through standardized interfaces without modifying their underlying code, compressing the cycle for new connections to the transformation system from an industry average of 30 days to 7 days, reducing transformation access costs by 60%. As of Q4 2024, Hyperdata has completed over 500 billion cross-chain data transformations, and through the 'Homogeneous Data Mapping Protocol (HDMP),' the asset data of the same user across multiple chains can be transformed into a unified view of 'holding type + liquidity frequency + risk rating,' improving the efficiency of developers calling cross-chain data by 90%.
2. AI adaptation transformation: Eliminating the 'transformation gap'
Hyperdata has built in the 'AI Feature Instant Transformation Protocol (AFTP),' which directly transforms raw data into AI-usable features, solving the transformation gap from 'data to AI':
• Real-time feature transformation: Based on a pre-trained lightweight feature model, it can complete the labeling of features such as 'user behavior sequence, asset volatility coefficient, contract interaction risk,' etc., within 100ms after cross-chain data transformation, generating standardized feature vectors compatible with TensorFlow and PyTorch. AI developers do not need to perform offline processing, achieving a 400% increase in transformation efficiency;
• Scenario-based feature templates: For 8 categories of high-frequency scenarios such as DeFi risk control, NFT valuation, and market predictions, predefined feature combination templates (e.g., 'cross-chain asset holding duration + Chainlink security score') are available for developers to directly call, reducing model training cycles from 15 days to 2 days.
Additionally, Hyperdata has achieved deep cooperation with Chainlink Scale, through the 'On-chain + Off-chain Data Fusion Transformation Protocol (ODFP),' real-time transforming off-chain data such as macroeconomic indicators and asset security ratings into AI-usable features, forming a 'full-dimensional feature package,' which improves the prediction accuracy of AI models by 18%-22% (for example, the prediction accuracy of DeFi bad debt rates increases from 82% to 98%).
Two, ecological landing: Tools and scenario support for dynamic transformation
The ecological value of Chainbase lies in lowering the threshold for using dynamic transformation through tools and expanding transformation scenarios through leading collaborations; all data comes from the publicly available ecological reports of the project:
1. Manuscript developer tools: The 'accelerator' for transformation efficiency
To enable developers to quickly reuse Hyperdata's dynamic transformation capabilities, Chainbase has launched the Manuscript tool suite (including a GUI visualization platform and CLI command-line tools):
• Visual transformation configuration: Developers can generate the complete logical flow of 'cross-chain data transformation - AI feature calling' simply by dragging and dropping, and the tool automatically transforms it into Solidity, Move, or Rust code, eliminating the need to manually write cross-chain interaction and feature processing scripts, enhancing development efficiency by 60%;
• Real-time transformation monitoring: A built-in data transformation monitoring panel allows developers to view the progress of cross-chain data, feature transformation status, and AI calling feedback in real-time, reducing problem identification time from 24 hours to 10 minutes.
Currently, Manuscript has served over 20,000 developers, with 40% focusing on AI-driven Web3 application development; over 8,000 projects have integrated Hyperdata's dynamic transformation capabilities, covering three core scenarios: DeFi (35%, such as cross-chain lending risk control systems), NFT (28%, such as asset valuation tools), and AI infrastructure (22%, such as on-chain behavior analysis platforms).
2. Deep cooperation with leading ecosystems: The 'expansion' of transformation scenarios
Chainbase's cooperation is not merely surface-level functional integration but embedding dynamic transformation capabilities into core ecological scenarios, achieving seamless value transformation of 'data - applications':
• Base ecosystem integration: As the officially recommended dynamic data transformation service provider for the Base chain, Hyperdata's cross-chain transformation protocol has been integrated into Base's OP Stack, with 60% of AI projects in the Base ecosystem (such as cross-chain asset monitoring tools, on-chain credit evaluation platforms) relying on its transformation capabilities, and the frequency of data transformation accounting for 28% of the total data demand in the Base ecosystem. Due to Hyperdata's low-latency transformation characteristics, the response speed of these projects has improved by an average of 50%;
• Coinbase CDP wallet integration: As one of the first data partners for Coinbase's embedded wallet (CDP), Hyperdata will provide 110 million Coinbase users with a dynamic transformation experience of 'on-chain data - AI services' - after users authorize their on-chain behavior data, they can receive real-time AI financial advice and personalized NFT recommendations based on Hyperdata transformation. The data transformation delay is controlled within 200ms, and the transformation benefits (in the form of $C) are credited in real-time.
Three, value mechanism: $C-driven transformation benefits distributed in real-time
The core of dynamic data transformation is 'instant transformation of benefits into contributor rewards,' and Chainbase constructs a real-time profit-sharing mechanism through the $C token, with all rules derived from the project white paper and smart contract audit reports:
• Token economic design: Total supply of $C is 1 billion coins, with TGE (Token Generation Event) completed in July 2025. 65% is used for ecological incentives (40% allocated to integrated projects and community developers, 12% rewards for data transformation node operators, 13% for user airdrops), and 35% for long-term development (17% allocated to early investors, 3-year linear unlocking; 15% belongs to the core team, 3-year linear unlocking; 3% for initial liquidity support);
• Real-time profit-sharing transformation: Achieving 'data transformation - profit distribution' instant linkage based on smart contracts - each time a data node completes a cross-chain transformation, it can receive a basic C reward in real-time; when data is transformed into AI features and called, nodes and developers can receive profit-sharing rewards in real-time (the profit-sharing ratio adjusts dynamically based on the transformation scenario, with high-value scenarios such as financial risk control having a profit-sharing ratio 2.5 times that of ordinary scenarios); 5% of API transformation call fees (paid in C) will be destroyed in real-time, and as the scale of data transformation expands, the scarcity of $C continues to increase;
• Market performance: C has been listed on major exchanges such as Binance, MEXC, and Bithumb, with the C/USDT trading pair on Binance 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 price range of $C is $0.2130 - $0.2925, which is about 55% lower than the historical peak price of $0.5445 on July 18, 2025, with a fully diluted valuation (FDV) of $187 million - $282 million, lower than similar data transformation projects (such as The Graph with an FDV of about $1.2 billion), presenting reasonable valuation space.
Four, future evolution: From dynamic transformation to industry value standards
Based on Chainbase's publicly available roadmap, its long-term development will focus on 'upgrading transformation capabilities and formulating industry standards,' with all goals derived from existing technical foundations and ecological scale projections:
1. Global data dynamic transformation: Integrating vertical field data sources such as IoT, supply chain logistics, and government compliance through the 'Cross-Domain Data Transformation Protocol (CDTP),' breaking the boundaries of 'only serving blockchain'; while introducing ZKML (zero-knowledge machine learning) technology to develop the 'Privacy-Preserving Dynamic Transformation Protocol (PPDP),' achieving real-time transformation of sensitive data in fields such as healthcare and finance while ensuring data privacy, with a plan to support over 50 types of data sources by 2026 and reduce data transformation delays to within 50ms;
2. C-end data value transformation: Launching the 'Personal Data Dynamic Transformation Protocol (PDEP),' users can independently authorize their on-chain behavior data (such as transaction records, asset holdings, social interactions), which are transformed in real-time by Hyperdata into the features required for AI services, and receive $C profit-sharing in real-time; concurrently developing 'data transformation authorization management tools,' allowing users to view data transformation records and adjust authorization scopes at any time, aiming to reach 10 million C-end users by 2026, forming a closed loop of 'personal data - AI services - profit feedback';
3. Industry transformation standard formulation: Jointly with the Ethereum Foundation, Base team, Chainlink, and leading AI companies (such as Anthropic) to release (Web3+AI data dynamic transformation industry standards), defining industry benchmarks for cross-chain transformation efficiency, AI adaptation transformation accuracy, and profit-sharing transformation delays, promoting the DataFi track from 'functional competition' to 'transformation efficiency competition.' The goal for 2027 is to complete 20 trillion data transformation calls, becoming the world's largest decentralized data dynamic transformation platform.
Summary
Chainbase's core competitiveness lies not in 'multi-chain data aggregation' or 'AI tool development,' but in achieving industry-leading 'transformation efficiency' for Web3 data value through Hyperdata - with cross-chain transformation delays ≤100ms, AI adaptation transformation efficiency increased by 400%, and real-time profit-sharing distribution, solving the industry's underlying pain points of 'difficult cross-chain transformation, costly AI transformation, and delayed profit-sharing transformation.'
Its ecological barriers stem from three points: first, over 20,000 developers and 8,000 projects are building applications based on dynamic transformation capabilities, forming a positive cycle of 'developers - projects - users'; second, deep integration with leading ecosystems such as Base and Coinbase allows transformation capabilities to cover more core scenarios; third, the real-time profit-sharing mechanism of $C ensures the long-term collaborative willingness of ecological participants.
Although $C is currently in a price correction cycle, combined with the growth prospects of the Web3+AI industry (with a market size expected to exceed $10 billion by 2025), Chainbase's first-mover advantage in data transformation efficiency, and its reasonable valuation of $187 million - $282 million, its long-term value lies in becoming the industry standard for dynamic transformation of Web3+AI data - this is both a core barrier that distinguishes it from similar projects and a key logic supporting its long-term development.
