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Automating Post-Review Content Corrections: Building Scalable Feedback Parsing, Reprocessing Infrastructure, and QA Interfaces for Global Media Localization
This project will help Aview International build an automated system that improves the quality and efficiency of translated and dubbed content. Right now, human reviewers leave feedback on content errors like bad subtitles or misaligned audio, but these changes must be fixed manually. The intern will design tools to turn that feedback into structured data, automate the correction process, and build a dashboard to track progress. This will make it faster and easier for Aview to deliver high-quality global content, reduce manual work, and support their rapid growth.
Faculty Supervisor:
Bilal Farooq
Student:
Partner:
Aview International
Discipline:
Business
Sector:
Arts, entertainment and recreation; Professional, scientific and technical services
University:
Toronto Metropolitan University
Program:
Business Strategy Internship
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