Alaya AI

Tasks are routed to contributors based on their track record in each subject area, not first come first served. Alaya describes the routing as swarm-inspired sampling, and the point is to reduce bias by pulling labels from many people instead of a small room. Contributors see tasks matched to their verified strengths, which raises the odds that specialised work lands with someone who can actually do it.

The auto-labelling toolset runs a first pass on images, text, and video, then hands uncertain cases to humans. Alaya built it on a three-layer optimisation architecture with RLHF fine-tuning, and says it handles both static and dynamic visual data in real time. For buyers, the practical effect is fewer paid human hours per thousand rows.

A client can fund a request with its own token instead of AGT, on-chain or off-chain. That matters for Web3 projects that already have a community holding their token and want to turn it into a labelling incentive. The pool model also means pricing is negotiated per dataset rather than published as a fixed rate.

General tasks are open to anyone. Specialised work, such as radiology images or uncommon dialects, needs a Medallion NFT in that field. Alaya awards these wallet-bound NFTs from performance rather than paperwork, and treats them as a private way to confirm expertise without asking for identity documents.

Contributors and token holders can stake AGT into auto-labelling model pools. Staking signals support for a model and adds a penalty for bad behaviour, since a malicious labeler risks the stake. Alaya frames this as a way for communities to fund model fine-tuning directly, and it doubles as the access gate for advanced task formats.

Data passes through ZK encryption and desensitisation before delivery, and the platform keeps contributor identities separate from the labels they produce. For a hospital or a fintech sending sensitive samples, that separation is often the difference between signing and walking away.