Bluwhale is a decentralized personalization protocol that incorporates web3 with AI and contextual data. Their mission is to develop the most user-centric open AI data network for dApps. ^[1] The current CEO of Bluwhale is Han Jin. ^[2]

Overview

The decentralized personalization protocol behind Bluwhale transforms user information into a queryable, vector-graph-based layer, consolidating data from various blockchain networks and organizing it around individual user profiles. This simplifies the user experience across decentralized applications (dApps), allowing users to optionally share their preference profiles with the dApps they use. ^[3]

When users choose to share their data, they become part of a revenue distribution system and are rewarded through the value-creation process. Conversely, if they decide not to share their personal data, they maintain their privacy and are treated as any other anonymous user within the application layer. This dual approach respects user autonomy while fostering a more user-centric and inclusive web3 ecosystem. ^[3]

BluWhale promotes interoperability, fostering collaboration and data sharing among applications. Security is a priority, with BluWhale AI ensuring verified and secure user data usage as permitted. The platform values user data based on market demand. ^[4]

Design

Bluwhale aims to create a user-focused data access layer with the following characteristics: ^[4][3]

• Interoperability: To maintain interoperability, incentives should prioritize end-user benefits to encourage loyalty to the advantages of an open network. Additionally, there is a secondary aim to promote long-term enterprise collaboration. This involves ensuring the availability of network-unique data, offering price incentives for early adopters, and implementing revenue-sharing mechanisms for ecosystem participation.
• Trust: The verification process of user and contextual data can be achieved through both passive and active methods. Active verification involves using tokens and social behavior to encourage users to authenticate their profiles with off-chain data, providing rewards for verified interactions. On the passive side, AI can be employed to collect, analyze, and differentiate authentic user activity from bots or malicious profiles.
• Fair Market Value: Data access is determined by user approval and valued based on fair market principles. The cost of accessing a particular user's data is influenced by enterprise demand, with pricing following the market dynamics of supply and demand. As interest in a user's data increases, the price goes up, reflecting a growing number of entities seeking access. To ensure overall ecosystem stability, the network imposes a nominal fee on each transaction.
• Data Availability: Enterprises may be willing to pay for data access, but the consumer has the ultimate authority to allow or deny data availability. The network ensures that once access is granted, the data remains accessible for queries as long as payments continue.
• Inflation/Deflation: A token burn mechanism is implemented to address possible deflationary effects, eliminating tokens with each enterprise query. This mechanism is essential for maintaining balance in the network's economy and sustaining token value.