Audit-grade verification for AI systems.

Verifiable Labs turns model evaluations into training-grade reward signals. The first product is audit-grade verification: fresh tasks, objective rewards, calibrated uncertainty, signed reports. The platform is reward infrastructure for post-training: paired traces, conformal intervals, and failure-mode data that feed directly into your RL loop.

• 25 live environments across compressed sensing, super-resolution, medical CT/MRI, phase retrieval, symbolic algebra, code execution, SQL, and long-context reasoning. Procedurally regenerated per call, classical-solver ground truth, conformal-calibrated rewards.
• verifiable CLI + Python SDK + hosted REST API — three on-ramps to the same protocol.
• vlabs-audit — single command turns any frontier model into a signed PDF capability report.

The SDK and the 25 environments are Apache-2.0: verifiablelabs/verifiable-labs-envs.

Hosted services (reward-model API, managed environments, attestation programme, self-hosted enterprise) are available separately — see the website.

• Site — https://verifiable-labs.com
• SDK / envs — https://github.com/verifiablelabs/verifiable-labs-envs
• PyPI — https://pypi.org/project/verifiable-labs/
• Zenodo (DOI 10.5281/zenodo.19786415) — https://doi.org/10.5281/zenodo.19786415

• General — hello@verifiable-labs.com
• Security disclosures — security@verifiable-labs.com
• Issues / contributions — verifiable-labs-envs/issues