Reliabl is a collaborative data-annotation platform that helps teams produce contextually aware AI training data by letting annotators and domain experts label together using custom taxonomies. The platform turns disagreement into a conversation, surfacing cultural nuance and subjective interpretations that conventional annotation tools average away.

Founded by AI-bias researcher Annie Brown and Stanford PhD Duncan McElfresh, Reliabl serves companies that need reliable training data with reduced bias and measurable fairness outcomes. The product covers data collection, flexible labeling schemas, team discussion, and dataset preparation for downstream model training.