10 Experts, 100M Data Points

Ten experts. A hundred million data points to review.

At 30 seconds per check, each expert faces roughly 40 years of full time work. Review a small sample and problems can slip through. Automate the checks and you need to know whether the AI understands what your experts consider good.

Sapien is an AI training data evaluation system guided by your team's expertise. It uses competing AI judges to extend your experts' judgment across more data, while keeping those experts involved. For teams building robotics, world models and physical AI, that means evaluating training data against the criteria their own experts define.

Your experts define good

The starting point is the people who already understand your data and the problem you're trying to solve. They know why an answer that looks reasonable might be wrong.

Sapien works with them to define the evaluation criteria and review a sample of your data. Their reasoning guides the instructions for AI judges. Their decisions and outputs provide reference answers to test against.

Your team decides what matters, which errors are acceptable and what a successful evaluation needs to show.

AI judges compete to apply that judgment

Worked examples help configure the judges. Qualification then uses a separate sample whose expert answers stay hidden. Each judge has to demonstrate that it can apply your team's criteria to new work.

Sapien compares their performance and selects a panel to review the wider dataset. Your team can set minimum agreement requirements and the evidence needed to qualify.

The judges assess items independently. Sapien combines their answers under the agreed consensus rules and records their results.

Your experts stay involved

Qualification is the start of the checks. Hidden spot checks continue to measure whether the judges match your experts' decisions. Judges that fall below the agreed standard can lose their place, and stronger performers can replace them.

If the AI judges do not meet the agreed confidence threshold or quorum, Sapien automatically escalates the item to the same human experts who set the standard.

Those experts keep checking the system as the data and the task change.

More reviewed data, fewer expert hours per item

Your experts focus on defining the criteria, checking performance and resolving difficult decisions. Qualified AI judges handle the wider volume.

That can get reviewed data into training sooner and catch problems before they waste compute. Expert time still needs a budget. Larger datasets mean more spot checks, and difficult data can mean more escalations.

A pilot should establish the benefit on your data: how closely the judges match your experts, how much expert time the process needs and how long the review takes.

Read more about how Sapien evaluates your data. If expert review is holding up your training pipeline, tell us about your dataset and how your team judges it.