In the world of Web3 data, there has always been an awkward "supply and demand mismatch": on one side, good data held by users and small developers "cannot be married off" — cross-chain transaction records and vertical scene datasets lie idle in wallets or servers, with no channels to connect to demand; on the other side, institutions and large platforms "cannot find good data" — they want "DeFi user risk profiles" or "NFT collection preference data", but they either cannot find it or the data is inaccurate or non-compliant. Most data tools only create "data shelves" without addressing "who needs what and who has what", leading to data value being stuck in the "disconnection between supply and demand". The core innovation of Chainbase is not to create a "shelf", but to act as the "precise matchmaker" for Web3 data supply and demand — helping suppliers "groom" their data into standardized "resumes", translating vague requirements for demanders into specific requirements, and using a secure mechanism to provide "marriage protection", ensuring that data and scenarios match efficiently and collaborate bidirectionally, which is the key to its differentiation in the field.
I. Grooming for suppliers: Transforming scattered data into "high-quality resumes" to solve the pain point of "not being able to find a match"
For data to find good scenarios, it must first "look presentable" — users' scattered transaction records and small developers' niche datasets, if not organized into standardized formats, will simply be disregarded by demanders. Chainbase's "data grooming platform" helps suppliers transform data from "raw" to "refined", while also building "exposure channels" to ensure that good data is accurately visible.
For individual users, "grooming" means "data profiling": automatically integrating users' scattered data across multiple chains (like DeFi transactions on Ethereum, NFT mints on Base, and gaming behavior on Sui), extracting core dimensions such as "asset scale, risk preference, and behavior characteristics", and generating standardized "user value profiles". For example, a certain user's ETH holdings, 30 days without overdue DeFi records, and preference for blue-chip NFTs will be organized into a "conservative Web3 user profile", marked with "applicable scenarios: DeFi risk control, NFT marketing". By October 2025, over 120,000 users had generated such profiles, with 68% being viewed by demanders, far exceeding the 0.5% exposure rate of scattered data.
For small and medium developers, "grooming" is "data productization": providing "data packaging tools" to help developers transform raw data from vertical scenes (like GameFi player development data or DAO voting records) into dataset products that come with "usage instructions, compliance certificates, and update frequencies". A small team's "Solana chain GameFi player strength analysis dataset" was packaged and labeled as "daily updates, covering 50+ chain games, compliant with CCPA", and was procured by three chain game platforms, increasing monthly revenue from $0 to $23,000C.
"Exposure channels" are equally critical: Chainbase builds a "data supply and demand platform", allowing suppliers to input "resumes" into the database with one click, while demanders can search based on scenario tags (like "DeFi risk control" or "NFT marketing"). The platform also proactively pushes matching data based on the historical procurement preferences of demanders — a certain NFT marketing platform once searched for "blue-chip NFT holder profiles", and the platform not only displayed relevant profiles but also pushed a "recent high-frequency trading NFT user dataset". After a trial, the platform directly procured it, and the supplier earned an additional $800C.
II. Translating for demanders: Turning vague demands into "clear requirements" to solve the pain point of "not finding good data"
Demanders seeking data often find themselves in a dilemma: "I want to reduce bad debts, but I don't know what data I need" — vague demand descriptions make it impossible to take action even in the face of massive data. Chainbase's role as a "demand translator" is to convert the institution's business goals into specific data requirements, then accurately match them from the supplier's "resume database" while also conducting "data quality inspections" to ensure that the data received is "qualified data".
The first step is "demand disassembly": breaking down the institution's business goals (like "reducing DeFi bad debt rates" or "improving NFT transaction conversion rates") into actionable data dimensions. For example, Aave proposed "reducing cross-chain collateral bad debts", and Chainbase disassembled it into three core data requirements: "real-time market value of cross-chain assets, recent 30-day staking volatility, and historical overdue records"; OpenSea wanted to "improve NFT search conversion", disassembling it into "user NFT browsing history, collection preference tags, and historical transaction price ranges". This disassembly transforms demands from "vague ideas" to "executable data search standards."
The second step is "precise matching": the platform selects data that meets the criteria from the supplier's "resume database" based on the disassembled requirements and also performs "priority sorting" — sorting by data freshness (e.g., real-time data prioritized), compliance (e.g., GDPR-compliant data prioritized), and matching degree (e.g., prioritizing those that fully cover three dimensions). Aave quickly found 20,000 "cross-chain asset health profiles" through this matching, resulting in a 32% reduction in bad debt rates after implementation, improving efficiency by 200% compared to manually finding data; OpenSea matched 15,000 "NFT user preference profiles", achieving a 45% increase in search accuracy and an 18% growth in transaction conversion rates.
The third step is "data quality inspection": After matching, data is not directly delivered but is verified through "multi-node verification" (21 high-stake nodes verify the authenticity of the data), "compliance scanning" (automatically checks for compliance with the privacy laws of the demanders' regions), and "timeliness validation" (ensures the data update interval does not exceed 10 minutes), thus preventing "false data" and "non-compliant data". A European medical AI institution once procured a tumor treatment dataset, and the platform found that some data was not anonymized through compliance scanning and promptly replaced it with a version compliant with GDPR, avoiding a potential fine of millions of euros for the institution.
III. Providing matching "protection": ensuring both supply and demand sides can "cooperate with confidence", addressing the pain point of "disputes"
After data supply and demand matching, problems are most likely to arise: demanders fear data leakage of privacy, while suppliers worry about not receiving their share after authorization; once problems arise, they can blame each other. Chainbase's "matching protection mechanism" serves as a "marriage guarantee" using technology and rules, allowing both parties to collaborate without worries, and also forming a positive cycle of "repurchasing".
First is "privacy protection": using ZK-SNARKs zero-knowledge proof technology, demanders cannot see the original data when using it, only obtaining "data validity results". For instance, Aave uses user cross-chain asset data for risk control and can only know that "the user's asset health meets standards" without seeing specific wallet addresses and holdings; a medical AI institution using treatment data to train models can only obtain "data feature parameters", unable to restore patient identity information. This "usable but invisible" model allows demanders to use it with confidence, while suppliers do not have to worry about privacy breaches — a certain user's DeFi data has been used by five institutions without any privacy complaints.
Next is "revenue sharing protection": presetting revenue sharing ratios through smart contracts (default 60% for suppliers, 20% for the platform, 20% for ecological maintenance). Each time data is accessed, fees are automatically distributed, with Layer2 settlement ensuring a transfer time of ≤1.5 seconds and Gas fees ≤0.001C. A user's NFT preference data was accessed by three marketing platforms, with monthly revenue sharing automatically credited at 1500-1800C, all recorded on-chain, eliminating the need for repeated reconciliation with demanders; after procurement, small and medium developers' datasets have revenue sharing directly deposited into their wallets, with no "accounting periods" or "deductions". A certain team's GameFi dataset has achieved a cumulative revenue sharing of $72,000C within three months of launch.
Finally, there is "after-sale protection": the platform will track the effectiveness of data usage, such as whether the bad debt rate has decreased or conversion rates have increased after demanders use the data, and then adjust matching strategies according to the effectiveness. If data performance does not meet expectations (e.g., if the conversion rate of matched user profiles is below 5%), suppliers will receive "optimization suggestions" (like supplementing with recent user transaction data), and demanders can request a data replacement. A certain chain game platform, which previously procured player behavior data with poor performance, was matched with a new "high-activity player dataset" within three days, resulting in a 30% increase in DAU, followed by an additional six months of procurement, forming a positive cycle of "matching-feedback-optimization-repurchase".
Conclusion: A bidirectional approach is the true closed loop of data value
The value of Chainbase's "precise matchmaker" has never been about "arranging marriages", but rather ensuring that "good data" finds the "right scenarios", and that the "right scenarios" access "good data" — suppliers no longer worry about data going unused, and demanders no longer struggle to find data. Under a safe and transparent mechanism, both parties can collaborate efficiently, allowing data value to naturally materialize.
This model aligns with the scoring logic of platforms like Binance for quality projects: "Technical practicality (ZK privacy, smart contracts), market health ($C liquidity stability, 24-hour trading volume exceeding $49 million, institutional holdings accounting for 50%), ecological sustainability (over 120,000 suppliers, 120+ demanders, 26,000+ developers)". It also addresses the core pain point of "supply and demand mismatch" in Web3 data. With the upcoming launch of Hyperdata Network 2.0 (which introduces a "supply and demand forecasting model" to anticipate popular data types), Chainbase will further improve matching accuracy and foresight. As Web3 data transitions from "unidirectional output" to "bidirectional collaboration", its "precise matchmaking" logic represents the core direction of the future data ecosystem.
