semantic spaces so teams can change or upgrade underlying models without re-embedding entire datasets. UniVec offers bridge-based embedding and chained conversion capabilities that route vectors through intermediary models when direct mappings are unavailable. Its services target technical teams building search, recommendation, and conversational AI applications that require consistent vector representations across heterogeneous AI stacks. The platform is delivered as developer-focused APIs designed to fit into existing data and model pipelines.