In the Web3 data track, most projects fall into the misconception of 'technology first, demand later': first piling on technical parameters, then searching for practical scenarios, resulting in 'flashy technology that users don't use'. Chainbase's core competitiveness lies in being a 'demand-driven' reverse practitioner—starting not from a technical blueprint, but from the real pain points of users 'not earning money from data', developers 'finding development too difficult', and institutions 'not being able to use data', constructing value with actionable solutions. This 'pain points-solutions-results' closed loop precisely aligns with the core evaluation criteria for quality projects regarding 'practical technology, healthy market, and ecological implementation', becoming the key for its continued breakthrough in the track.
1. User pain point: data 'idling like waste', solution: let every piece of data 'generate passive income'.
The core demand of ordinary Web3 users is simple: 'Can the transaction, NFT, and DeFi data I generate not just lie idle in my wallet, but also earn some money?' However, past problems included: unclear rights confirmation fearing misuse, broad authorizations fearing data leaks, and delayed profit sharing fearing losses. Chainbase's solution is to build a 'zero-threshold passive income system', allowing users to generate continuous earnings without needing to understand technology.
First, 'no need to manage rights confirmation': when users generate any data on-chain (such as ETH transfers or NFT purchases), Chainbase automatically generates a unique hash 'data certificate', binding it with the wallet address through a smart contract, with rights confirmation records being updated to the chain in real-time—by opening the Chainbase wallet plugin, users can see 'which data belongs to them and who has accessed it', eliminating the need for manual operation and avoiding the risk of 'data being secretly used'.
Second, 'authorization is very flexible': users can set 'refined authorization rules' in the plugin, such as 'only allowing a certain financial app to view BTC holdings for the last 30 days, not allowing access to transaction counterparties' or 'authorizing AI models to train with data, with a profit-sharing ratio not lower than 60%', and can also set 'authorization expiration dates' that automatically revoke permissions upon expiration, eliminating worries about 'one-time authorization for permanent use'.
Finally, 'profit sharing arrives in seconds': every time data is accessed, earnings are automatically transferred to the wallet through Layer2 smart contracts, with gas fees as low as 0.001C and arrival time of 1.2 seconds. A certain user authorized DeFi transaction data to 3 platforms in Q3 2025, accumulating 2800 accesses and receiving 1680C; moreover, $C has solid liquidity on 14 exchanges such as Binance, with a 24-hour trading volume exceeding $47 million, allowing earnings to be liquidated at any time without waiting for 'monthly settlements'. This 'turning idle data into passive income' solution precisely targets users' pain points of 'wanting to earn money while fearing hassle'.
2. Developer pain points: Developing 'redundant solutions', solution: let technical efforts focus on 'innovation' rather than 'adaptation'.
The core demand of Web3 developers is 'can we spend less time on basic adaptation and more time on core functionality?' In the past, developing data-related applications required 3 months to integrate multiple chain APIs and manually label AI data, which was costly and time-consuming. Chainbase's solution is to provide a 'one-stop development toolbox' that thoroughly handles the basic work, allowing developers to 'develop with ease'.
First, 'no need to write cross-chain adaptations': Hyperdata Network has already completed the underlying adaptation for over 200 public chains and Layer 2s like Ethereum, Base, and Sui, allowing developers to call multi-chain data using a single 'universal API' without needing to write adaptation code for each chain. A certain cross-chain DEX team originally needed 3 months to develop a 'multi-chain asset dashboard', but with this API, they went live in 2 weeks, achieving an 80% increase in development efficiency and saving 60% in labor costs, with daily access volumes exceeding 100,000 times after launch, quickly seizing the market.
Second, 'AI data does not need labeling': the Manuscript toolchain can automatically convert hash logs, contract bytecode, and other unstructured data into structured tensors directly usable by AI models, covering 12 dimensions including 'address activity' and 'asset association'. A certain team developing Web3 fraud detection AI originally needed 72 hours for manual data labeling, but now with Manuscript it takes just 4 hours, shortening the model training cycle by 90% and increasing accuracy from 54% to 89%. After going live, it was purchased by 5 DeFi protocols, generating monthly revenues exceeding 30,000 $C.
Third, 'development costs have subsidies': Chainbase has allocated 400 million C to establish a developer incentive fund, distributing rewards based on 'access volume + user rating'—breaking 1 million accesses earns 50,000 C, breaking 100 million accesses earns 1.5 million C, and developers can also receive 6 months of continuous profit-sharing. A certain team's 'cross-chain data risk scoring tool' had a monthly access volume of 120 million times, not only receiving a one-time incentive of 1.5 million C, but also an additional monthly profit share of 80,000 $C, sufficient to cover the team's operational costs for the entire year. This 'helping developers save time and subsidize costs' solution precisely addresses the pain points of 'difficult development and slow monetization'.
3. Institution pain points: data is 'difficult to use and compliant', solution: let data directly 'serve business objectives'.
The core demands of Web3 institutions (such as Aave, OpenSea) and traditional enterprises are 'can data directly solve my business problems? For example, reducing bad debts, increasing sales, saving costs?' The past issues were: data was too scattered to use, concerns about compliance risks when used, and no results when used. Chainbase's solution is to 'customize data solutions based on business objectives', allowing data to be directly transformed into business outcomes.
For Web3 financial institutions, the solution is to 'reduce risk': after Aave integrates Chainbase's 'real-time cross-chain collateral monitoring data', it can dynamically adjust the collateral ratio—when the asset price on a certain chain fluctuates by more than 5%, the data triggers a real-time alert, allowing the protocol to adjust the collateral ratio within 10 seconds, directly reducing bad debt rates by 30%; a certain cross-chain payment platform uses its ZK privacy solution to encrypt user identity information and transaction data separately, complying with Singapore's MAS regulations while reducing user privacy complaints to zero, and doubling the business coverage.
For the Web3 ecosystem platform, the solution is to 'improve efficiency': OpenSea uses Chainbase's 'NFT Feature Map' function to generate dynamic rarity scores by analyzing metadata and transaction history—when users search for 'high-potential NFTs', they can directly see key data like 'historical transaction prices and holder address activity', improving search accuracy by 40% and increasing transaction conversion rates by 15%, with monthly transaction volume increasing by $20 million; a certain metaverse platform used its 'virtual and real asset data collaboration tool' to bind on-chain NFTs with metaverse scenarios, increasing user retention by 35% and NFT item trading volume by 50%.
For traditional enterprises, the solution is to 'reduce costs': a certain automotive parts supplier used Chainbase to put logistics data on-chain, generating 'trustworthy logistics assets'—banks no longer need to conduct on-site inspections; they can lend based on on-chain data, reducing financing rates from 15% to 8%, and shortening loan processing times from 15 days to 24 hours, saving $210,000 in financing costs annually; a European medical alliance used its 'privacy computing + data rights confirmation' solution to convert tumor data from 10 hospitals into 'de-identified assets', complying with GDPR while improving AI diagnostic accuracy by 32% and shortening the new therapy development cycle by 6 months. This 'data directly serving business' solution precisely addresses the institutions' pain points of 'wanting results while fearing hassle'.
Conclusion: Demand-driven value is the core of quality projects.
The competitiveness of Chainbase has never been about 'supporting more chains than others or having an extra technical feature', but rather about 'accurately responding to the real needs of each role'—users want to 'easily make money', so we provide 'automatic rights confirmation + real-time profit sharing'; developers want to 'develop efficiently', so we offer 'one-stop tools + incentive subsidies'; institutions want to 'improve business efficiency', so we deliver 'custom solutions + tangible results'.
This 'demand-driven' logic perfectly aligns with the core evaluation criteria for quality projects: technology must be practical and solve real pain points; the market must not be speculative, with $C liquidity and institutional holdings being sufficiently healthy; the ecosystem must not be mere promises, with actual benefits for users, developers, and institutions. With the upcoming launch of Hyperdata Network 2.0 (introducing the 'business demand-data solution' intelligent matching feature), Chainbase will respond even more accurately to ecological demands, transitioning data value from 'passive satisfaction' to 'active matching'. As the Web3 data track shifts from 'technical competition' to 'demand competition', Chainbase's positioning as a 'demand responder' is precisely the core direction for the future data ecosystem.
