Verifiable contribution receipts
The network records each human contribution as a verifiable receipt with preserved metadata, so records can be traced back to their source without exposing user identities.
The Data Foundation is an open protocol for sourcing, proving, processing, and licensing real human data for AI training, with auditable provenance and rights-cleared datasets.
The DATA Foundation presents itself as an open protocol and data network for sourcing, proving, processing, and licensing real human data for AI training. Its homepage positions the network as infrastructure for datasets that need clear provenance, public auditability, and rights defined at the point of creation.
The site’s product surfaces point to a workflow that starts with human contribution, produces tamper-proof receipts and provenance records, and then turns those inputs into licensable or model-ready datasets. Trace handles verification and auditability, Kled supports opt-in data marketplaces, and apps like Numo focus on structured contributor tasks such as voice data collection.
The network records each human contribution as a verifiable receipt with preserved metadata, so records can be traced back to their source without exposing user identities.
Trace writes to a blockchain-based ledger that keeps an immutable audit trail and supports public verification of entire datasets.
The platform processes raw inputs into model-ready datasets and emphasizes quality enforcement, labeling, and dataset readiness for AI buyers.
Confidential Data Rails support programmatic gated access for sensitive information, allowing controlled access without publishing raw data openly.
The network supports licensing, permissions, and contributor payment flows so data can be sourced and exchanged with rights defined up front.
The site shows multiple product entry points, including Trace for verification, Kled for opt-in marketplaces, and apps such as Numo for task-based contribution.
AI labs can source transparent, consent-based datasets and verify where data came from before using it in model development or evaluation.
Contributors can upload their own data through marketplace or app flows and receive payment for eligible contributions.
App developers can build on the protocol to handle ownership, licensing, and payment rails without assembling those systems from scratch.
Teams handling sensitive information can use gated access and confidential rails to control who can see or use data.
Researchers and auditors can inspect receipts and dataset records to confirm consent, provenance, and dataset completeness at the network level.
The site presents The DATA Network as an open protocol for sourcing, proving, and processing real human data for AI training. It connects capture, provenance, licensing, and processing so datasets can be audited and used commercially.
The homepage describes Trace as the place to verify datasets and audit receipts on the network. It supports public proof, immutable records, and an audit trail for datasets and contributors.
The site shows Kled as a large opt-in human-data marketplace where people upload their own data, give permission for its use, and get paid. The records are auditable on the Data Network.
The blog announcement says Numo is a consumer app for contributing AI training data, starting with voice tasks in Bengali, Hindi, Tamil, and Telugu. It is in early access and is designed to reward eligible contributions.
The pricing page at `story.foundation/pricing` returns a 404 page not found response in the collected evidence, so no public plan information is available from that URL.