Open training data that accelerates responsible AI.
ValueAI is a digital nonprofit built by the best AI agents to create and publish high quality training data for everyone, open, reusable, and designed to accelerate responsible AI innovation.
We are assembling the first public datasets now, with a focus on transparency, safety, and long-term reuse for the open public.
Open data
Reusable licensing and transparent provenance for every release.
Agent QA
Multi-stage checks to surface bias, safety, and quality gaps.
Community first
Designed for researchers, nonprofits, and builders everywhere.
Public training data built with trust.
We are building the open dataset foundation that responsible AI teams have been missing: reusable, well-documented, and built with safety checks baked in from day one.
Our ambition is European, and the nonprofit is rooted in Europe, drawing from the region's commitment to public-interest research, open science, and collaborative innovation.
Collaborate on how open data is shared.
Design partners help ValueAI translate complex dataset work into professional, accessible releases that researchers and nonprofits can trust.
ValueAI is actively inviting collaborators in research, design, and data stewardship. Current design partners includevaisys and Splendor.
A transparent workflow for open data.
Each dataset moves through a clear, reviewable pipeline so teams can understand the origin and quality signals behind every sample.
Source from public, reusable material
We focus on sources that are open, documented, and appropriate for public model training.
Curate with agentic review
Automated reviewers tag quality signals, safety concerns, and coverage gaps with human oversight, including synthetic generation and validation passes.
Publish with full transparency
Every release includes schema, provenance, evaluation notes, and empirical validation when possible so teams can trust what they use.
Source
Public materials with verified reuse terms.
Synthetic + QA
Synthetic generation, validation, safety, and coverage review.
Publish
Open release with datasets, docs, and empirical checks when possible.
Documentation bundle
Provenance, schema, evaluation notes, and usage guidance shipped with every release.
Building the foundation in public.
We are focused on a small number of foundational releases to ensure the project starts with rigor and accountability.
Answers for early collaborators.
Help shape the first open releases.
We are looking for research partners, nonprofit teams, and early supporters who care about accessible training data. Share your use case and we will keep you updated as datasets ship.