AI training data for frontier labs

Built by vetted PhD and domain experts across LATAM. GDPval, Terminal-Bench, and STEM.

Request sample dataset

What's in Athyna Intelligence’s dataset library?

Terminal-Bench

Coding tasks built as hermetically reproducible, Docker-pinned environments, run through the Harbor harness.

GDPval

Tasks authored to GDPval spec: real professional deliverables, a document review, built and graded against expert-quality output.

STEM reasoning

Original math, physics, chemistry, biology, and other STEM tasks, each traced to peer-reviewed sources with DOIs.

From off-the-shelf samples to custom datasets

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Review a free sample

A small batch of tasks in the categories you care about, sent directly by the team at no cost, to evaluate format and quality first.

License the full dataset

License the larger dataset behind it.

Request a custom build

Built to your task spec.

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Frequently asked questions

What is Athyna Intelligence?

Athyna Intelligence is a specialized part of our platform that connects expert talent with companies that need support in training AI models. We match PhD and domain experts from Latin America with teams looking to build data sets, evaluate models, strengthen reasoning tasks, and accelerate AI development. It is a simple and fast way to scale AI training with vetted global talent.

What is Athyna Intelligence's dataset library?

A collection of off-the-shelf and custom AI training datasets across GDPval, Terminal-Bench, and STEM, built by vetted domain experts.

Can datasets be customized for our use case?

Yes. After reviewing a sample and the full dataset, labs can request a custom build scoped to their own task spec, sized and priced to fit.

How does Athyna Intelligence help frontier AI labs?

Labs use Athyna Intelligence for off-the-shelf and custom datasets, plus access to PhDs and domain experts who author and review tasks in their own field.

Who builds Athyna Intelligence's datasets?

PhDs and domain experts based across LATAM, each vetted for subject-matter depth before writing a single task.

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