Data Training

Data Training Pipeline

When users interact with the platform, their contributions are logged with clear consent. Before the data is used, it goes through several steps. Filtering removes irrelevant or sensitive content. Anonymization strips out identifiers so that no one can trace it back to an individual. Feedback from users, such as ratings or simple evaluations, is added to guide the learning process.

The pipeline supports both larger batch updates and smaller online adjustments. This way, AI Avatars can improve continuously while the overall system remains stable.

Model Architecture

AIAV avoids building a single, one-size-fits-all model. Instead, it is designed as a set of modules: a core for conversation, a memory layer to keep context, a persona layer that shapes tone and expertise, and a reasoning component for more complex tasks. Because these parts are modular, developers can add or replace them to create specialized Avatars for gaming, learning, or other services.

Decentralized and Federated Learning

To reduce dependence on central servers, AIAV makes use of federated learning. Local nodes, which can be run by community members or partners, process data without sending raw information to the network. Only updates to the model are shared back. This setup protects privacy, makes the system more resilient, and gives contributors the chance to earn rewards when they share computing power.

On-Chain Verification Transparency is supported by recording key metadata on the blockchain. Time, type of contribution, and reward scores can be verified publicly, while sensitive training data remains stored off-chain. This way, anyone can confirm that a reward was issued for a real contribution without exposing private information.

Security and Privacy Every layer includes safeguards. Data is encrypted both in transit and at rest. Users have clear controls to view, limit, or revoke how their contributions are used. Smart contracts that manage reward distribution are open to review and subject to audits. A dispute process also exists, so low-quality or misused data can be flagged before it enters the training cycle.