Translatica: A Voice-Preserving AI Translation Platform

This thesis presents the design, development, and strategic decisions of Translatica, a voice-preserving AI translation platform. In contrast to conventional translation tools that prioritize textual accuracy at the expense of speaker identity, Translatica seeks to amplify global communication by preserving the original speaker’s tone, emotion, and personality throughout the translation pipeline. The system integrates transcription, context-aware translation, and neural voice replication to produce emotionally expressive, multilingual video outputs. Originating from a hackathon focused on educational technology, Translatica evolved into a scalable solution seeking to bring accessible content for everyone. Its primary use case began in education but now spans creators, enterprises, and institutions seeking culturally resonant global communication. This work analyzes current market trends, competitor limitations, and the rising demand for speech-to-speech translation in education and media. It explores user interface design decisions, business model strategies, and ethical considerations in deploying voice synthesis technologies. Reflections from the development process highlight the importance of clearly defined problem statements and collaborative iteration in building impactful, user-first AI systems. This thesis represents one component of a broader collaborative project developed alongside two other engineers. While this paper focuses on the business strategy, user interface design, and ethical implications of the platform, the complementary theses address backend engineering, software infrastructure, and the training and deployment of AI models powering Translatica’s translation pipeline.