What is Tagger (TAG)?

TLDR Tagger (TAG) is a decentralized AI data solutions platform that combines blockchain technology with crowdsourced human input to streamline data labeling, authentication, and trading for AI development.

1. Purpose: Solves inefficiencies in AI data workflows via decentralized collaboration.
2. Technology: Uses blockchain for data rights management and DeCorp smart contracts.
3. Tokenomics: $TAG rewards contributors for tasks like data labeling and review.

Deep Dive

1. Purpose & Value Proposition

Tagger addresses the bottleneck in AI development where 80% of resources are spent on data preparation. By decentralizing tasks like data labeling and collection, it creates a permissionless workforce to handle specialized datasets (e.g., satellite imagery, sensor fusion). This eliminates centralized intermediaries, reduces costs, and accelerates AI training cycles.

2. Technology & Architecture

The platform uses a three-layer system:
- Standardized Datasets: AI Copilot tools assist workers in labeling, ensuring quality via peer review.
- Data Passport: On-chain authentication tracks ownership, consent, and usage rights for datasets.
- DeCorp Contracts: Smart contracts automate payments, task allocation, and revenue sharing, enabling cross-company collaboration without traditional management layers.

3. Tokenomics & Ecosystem

$TAG is earned by contributors for completing tasks (e.g., labeling, reviewing) and scales with account levels. Rewards follow formulas incorporating halving coefficients and accuracy metrics. Partnerships like Huawei Cloud and BlueSky Carbon Group demonstrate real-world use cases, with payments settled in stablecoins like USD1 and yield opportunities via DeFi integrations (Lorenzo Protocol).

Conclusion

Tagger redefines AI data infrastructure by merging decentralized workforces with blockchain-based rights management. Its hybrid model—leveraging AI assistance and crypto incentives—positions it as a bridge between Web2 enterprises and Web3 efficiency. How will its DeCorp framework evolve as AI demands more niche, high-quality datasets?