What does Sapien do, and what problem does it solve?
Sapien provides a 'Proof of Quality' (PoQ) platform for evaluating AI work through human judgment amplified by agents. It solves the problem of judging AI output quality where ground truth is absent, ensuring a shared standard, measuring reviewer agreement, and turning expert judgment into a reliable evaluation process. The core problem is that while AI makes output abundant, reliable judgment hasn't scaled with it.
What features does Sapien offer?
Sapien offers a PoQ workflow that includes defining a quality rubric, adding work for review, and choosing experts. Its key features are independent consensus review, a Quality Score against the rubric, a Consensus Score showing reviewer agreement, and a Proof Report with detailed evidence. It is used for data quality, model output evaluation, agent behavior review, and prompt quality comparison.
How is Sapien priced?
The evidence does not provide information about Sapien's pricing or packaging. The homepage offers to 'Request a pilot' but does not list specific pricing plans.
What is Sapien used for, and in what situations?
Sapien is used to measure and verify the quality of AI work for data, models, agents, and prompts. It is applied in situations where the 'right answer' depends on judgment rather than simple correctness, such as measuring data label and dataset quality, evaluating model outputs like answers and recommendations, reviewing agent decisions, and comparing prompt quality. The result helps teams act before sharing their work.
Who is Sapien for?
Sapien is for teams and organizations that develop or use AI models and agents. It is designed for teams that need to establish quality standards for AI work, evaluate outputs based on expert judgment, and have a clear process for when AI work is ready to ship, especially when traditional ground truth is not available.
What is Sapien?
Sapien is a platform that provides 'Proof of Quality' (PoQ) for AI work. It helps teams measure whether AI outputs—like data labels, model answers, agent decisions, and prompt evaluations—meet a defined quality standard using a process of rubric setting, expert review, and consensus scoring.