The global AI industry is starving for high-quality training data — but the existing pipeline is broken at every layer.
Companies pay data brokers thousands of dollars for stale, unverified datasets with no provenance and no accountability. Individual contributors generate enormous amounts of valuable data every day and receive nothing in return. And when labelling is needed, centralised annotation farms are opaque, expensive, and trivially gameable.
Data Daddy fixes all three simultaneously. We built a decentralised data marketplace on Base where companies post requests, verified contributors earn crypto for submitting responses, and a staking-based labelling pool enforces quality through economic incentives — not trust.
The labelling mechanism is the core insight: labellers stake ETH to submit a label, and only the majority consensus wins the pool. Bad actors don't just get ignored — they lose money. This makes dataset quality a direct function of financial skin in the game, removing the need for any centralised arbiter.
Privacy is enforced at the identity layer using Anon Aadhaar ZK proofs, letting contributors prove they are real, verified Indian citizens on-chain without revealing any personal data. Companies get demographic confidence; contributors keep their privacy.
The result is a trustless, permissionless data economy — where data quality is cryptographically enforced, contributor earnings are automatically settled on-chain, and no single entity controls the pipeline.