Alignment with DF goals (BGI, Platform growth, community)

High-quality, scalable training data is a bottleneck for AGI. CrowdLens creates a decentralized annotation marketplace on SingularityNET + Fetch.ai. Annotators onboard with DIDs, earn soulbound reputation tokens, and stake utility tokens to access tasks. Each labeled dataset is hashed to IPFS and minted as an NFT, embedding provenance and licensing. Quality is driven by on-chain peer review and active-learning loops: LLM-generated pre-labels flag edge cases for expert adjudicators. Smart-contract escrows release micropayments instantly upon milestone validation, with gas abstracted via meta-transactions. A DAO of annotators and enterprises governs fees, staking ratios, and review thresholds. Fetch.ai multi-agent protocols automate task discovery and workload balancing, creating a self-optimizing ecosystem where contributors retain IP rights and earn downstream royalties-all accelerating AGI research.

Problem description

Current annotation services are centralized: contributors face opaque workflows, delayed or batch payments, and zero ownership over their labeled data. Enterprises struggle with inconsistent label quality, no verifiable provenance, and lack of scalable governance. This fragmentation limits AGI progress and alienates the very communities that drive data-centric AI research.

Proposed Solutions

• Onboarding & Identity: Annotators register via DIDs, earn soulbound reputation, and stake tokens to qualify.

• Data Minting: Labels are hashed to IPFS and minted as NFTs with embedded licensing.

• Quality Assurance: Smart-contract peer reviews + AI active-learning flag edge cases for expert adjudication.

• Payments & Governance: Milestone payments release instantly; DAO votes set protocol parameters.

• Fetch.ai Agents: Autonomous agents match tasks to annotators and optimize throughput.