AI-Powered Content Ingest Optimization
How an automated content analysis pipeline reduced a streaming service's content-to-live time from 24 hours to just 2, saving over $800k annually.
The Story
StreamFlow TV, a leading video-on-demand platform, faced a significant operational bottleneck in its content pipeline. The manual process of ingesting new content—which involved transcoding, quality control checks, metadata tagging, and subtitle generation—took an average of 24 hours per asset. This lengthy turnaround time delayed the release of new content, frustrating both content partners and subscribers. The process was not only slow but also required a large team of operators, leading to high operational costs and a risk of human error in metadata and quality assessment.
To maintain its competitive edge, StreamFlow TV needed to radically accelerate its content-to-live workflow. The strategic objective was to automate the entire ingest process, leveraging AI to perform tasks that were previously manual, thereby increasing speed, improving metadata accuracy, and reducing operational overhead.
What Did Medha Soft Do
Medha Soft was engaged to deliver an end-to-end AI-powered content ingest and analysis platform. Our team of media technology and AI experts worked closely with StreamFlow TV's broadcast engineers to design and deploy a fully automated pipeline.
Automated Ingest & Transcoding Workflow
We built a cloud-native workflow that automatically retrieved source files, triggered parallel transcoding jobs for multiple resolutions, and performed automated quality control checks to detect video and audio artifacts.
AI-Powered Metadata Generation
We integrated several AI models to analyze the video content. This included speech-to-text for automatic subtitle generation, scene detection for creating video previews, and object/celebrity recognition to generate rich, searchable metadata tags, vastly improving content discovery.
Centralized Management Dashboard
We developed a simple web interface that allowed operators to monitor the automated pipeline, review AI-generated data, and manually override any step if necessary. This provided full visibility and control over the entire process.
Data Analysis Chart
The chart below demonstrates the dramatic reduction in average content ingest time (in hours) after the implementation of the AI-powered pipeline compared to the previous manual workflow.
Chart Caption: Average Content Ingest Time (Hours) Before and After Automation.
The Results
The AI-powered pipeline transformed StreamFlow TV's content operations, delivering significant improvements in speed, cost, and content discoverability.
90% Reduction in Ingest Time: The average time from content delivery to being live on the platform was reduced from 24 hours to just 2 hours.
$800k+ Annual Cost Savings: Automation significantly reduced the need for manual operators, leading to substantial recurring savings in labor costs.
300% Increase in Metadata Richness: The AI-generated tags made content more discoverable, leading to a measurable increase in user engagement with library content.
Customer Reviews of the Case
Medha Soft's AI pipeline has been a game-changer. What used to take a full day and a team of people now happens automatically in a couple of hours. It has allowed us to scale our content library faster than we ever thought possible.
Aisha Khan
VP of Media Operations, StreamFlow TV
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