We build policy-adaptive content classification models that read your policy and label content against it, instead of forcing you into a fixed taxonomy. Platform teams keep authoring the policies they always have; the classifier follows along.
Our flagship is the CoPE model family (COntent Policy Evaluator): accurate, efficient, single-pass models fine-tuned for policy interpretation. Provide a free-form policy alongside the content, and CoPE returns a label calibrated to that policy.
For a more rigorous understanding of how our models work, check out this research paper: CoPE: A Small Language Model for Steerable and Scalable Content Labeling
This is the official Zentropi HuggingFace repo. New CoPE model checkpoints and accompanying datasets are released here.
Models
For our hosted API and policy authoring platform, visit zentropi.ai.