Machine-learning augmented content preparation services

Traditional human-driven approach to content preparation is time-consuming, expensive and prone to manual errors. When TV networks, OTT content creators and distributors want to scale up rapidly, and process large volumes of content, manual content preparation are a major bottleneck.

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1. How Amagi's Tornado works
2. Some of the critical use cases of machine-learning driven broadcasting like Factory-scale content segmentation, near real-time live to VOD, linear TV OTT ad monetization
3. Key benefits and feature highlights