Funding
Funding not disclosed
Founders
Product
Problem
Training foundation models requires massive amounts of high-quality labeled data, but human annotation is slow, costly, and often introduces noise, limiting model performance and scalability.
Solution
0× verify provides verifier‑grounded training datasets in text, image, video, and speech that are generated and validated by formal proof systems, simulators, executable tests, and oracle databases. Each dataset includes a signed manifest with sample‑level lineage and an open‑source verifier that users can re‑run to confirm data integrity. Service level agreements are encoded in the manifest, guaranteeing automatic credit for any verification failures, zero contamination, and source attestation. By replacing manual labeling with oracle‑driven data, the company enables developers to train larger, more reliable models without the bottlenecks of human annotation.
Target Audience
Primary customers are AI research labs, foundation model developers, and enterprises building large‑scale language, vision, video, or speech models that require trustworthy, high‑quality training data.
Features
- Verifier‑grounded datasets across four modalities with sample‑level lineage and signed manifests
- Open‑source, re‑runnable verification tools (MIT licensed) that validate each sample within ~38 minutes
- SLA guarantees: 24‑hour verification, zero benchmark contamination, full source attestation, and 10× credit for verification misses
- Large‑scale text dataset: 2.4 B verified tokens, 1.28 M samples, 118 M reasoning traces, multilingual support (26 languages)
- Automatic credit and remediation mechanisms encoded directly in the data contract
- Easy integration via a Python API (e.g., `load` and `verify` functions) for seamless dataset consumption