Ship faster.
Catch data issues
before training.
Proof of Quality helps AI teams review training data at scale, using AI judges tested against human experts. Your team defines what the data needs to pass.
Start with a conversation. No dataset upload required.AI judges are tested against your experts.
See each step in detail →Experts apply your rubric.
Your experts review a sample and agree on reference judgments.
AI judges are tested.
Candidates are compared with expert answers they have not seen.
Selected judges review.
All selected judges review each item. Uncertain consensus goes to experts.
Experts keep checking.
Blind expert checks detect drift. Judges that fall short are replaced.
Scale review.
Keep your quality bar.
Get reviewed data into training sooner. AI judges compete to match your experts' judgment, then review every item as a panel. Experts resolve uncertain decisions and run blind checks, while judges that fall short are replaced.
See how PoQ works →Built around your data and your experts.
Our current focus is training data and annotations for robotics, world models and physical AI.
Connect through API, MCP or a storage bucket. We agree the access your pilot needs.
Human judgment should guide how AI develops.
Sapien is building infrastructure to help make AI trustworthy and aligned with human intent. We start with data, with a longer term plan to apply the same discipline to model outputs, agent decisions and deployed behavior.
Start with one dataset.
Your dataset
A representative sample and your acceptance criteria.
The pilot
An agreed scope, expert involvement and measures of success.
The results
A reviewed sample with the reason behind each decision.
Your results include review time, expert effort and agreement with your experts.
Questions
Training data and annotations for robotics, world models and physical AI, where your experts can judge quality from the data itself.