Turning fragmented annotation into scalable impact for a global networking platform

A leading professional networking platform was struggling to manage data annotation work across multiple projects and languages. Internal teams were stretched thin, and a previous vendor had failed to solve key problems around efficiency, resource utilization and coordination. With annotation volumes continuing to increase, they needed data annotators and linguistic help to support AI-driven recruitment and talent workflows at scale.. That’s when they turned to TrainAI by RWS.. By moving from a fragmented, ad hoc approach to a fully managed service model, TrainAI helped them streamline processes, reduce annotator idle time to less than 5% (often < 1%) and expand confidently from English into French, German, Spanish and Portuguese..