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A Top 10 Global Media conglomerate automates their Media Operations
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Client Profile

The Client is a Global top 10 mass media conglomerate well known for creating entertainment experiences that stimulate conversation and culture. With over 170 networks reaching 700 million global subscribers, this American media company is one of the oldest in the business.

The Challenge

To hit better monetization opportunities, the Client wanted to deliver 3 years worth of TV show content to various OTT media-service providers like Netflix, Hulu, Amazon, etc.

For this to happen, every TV show content package had to be compliant with prescribed OTT guidelines and standard specifications. If the content package failed compliance, it was rejected and returned to editorial teams for rework.


Key Highlights

~100% search automation

Editorial teams can directly jump to the problem areas and need not search for them.

80% automation of repetitive edits

The AI learned from editorial metadata and did the redundant edits by itself through active learning.

5X boost to OTT readiness workflow

It took 45 min to process an average video file earlier, post-implementation it took 6 min.

More time for creativity

The hours returned could be utilized for more strategic & creative editorial work.

 In old video workflows, the editing team manually identified problem areas within each video. The process was effort-intensive and repetitive, and couldn’t be called an excellent example for creative bandwidth allocation.

 Long turnaround time also meant fewer files processed at high workflow costs. Not to mention manual editing and QC within content assets was tedious & prone to error.

Our Solution

The solution entailed a pre-trained custom AI model which performed video similarity. This led the Client to completely automate problem identification and automate up to two-thirds of redundant editorial tasks. The AI model pinpointed the error, so teams could focus on fixing alignment issues and glitches within the file.

An Auto AI platform augmented the existing workflow by dynamically training a base model. This model made editorial decisions which were previously manual. For example, conforming subtitles and audio to the master copy.

The model learned over time from editorial actions and improved in accuracy.

By training AI systems to take over 80% of its repetitive manual reliant tasks, AthenasOwl Smart Workflows helped the client boost its efficiency by 5x.

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