Training Your Own LLM: The Untapped Value of Media Archives

Voices of Video

The AI revolution in media production is moving beyond marketing hype into practical applications delivering real value. Ryan Jesperson from Cires21 takes us deep into how their Media Co-Pilot platform is transforming workflows for tier-one broadcasters through thoughtfully implemented artificial intelligence.
Unlike basic AI integrations that simply bolt onto existing systems, Media Co-Pilot addresses the nuanced needs of professional media organizations by training models specific to broadcast content. This approach solves the "garbage in, garbage out" problem that plagues many rudimentary AI implementations, delivering broadcast-quality results for transcription, translation, face detection, and scene analysis.
What makes their approach particularly valuable is the hybrid integration model combining public LLMs with private, custom-trained solutions. National broadcasters with decades of archival footage aren't just protecting their workflows – they're safeguarding valuable intellectual property that could otherwise be scraped and utilized by competing services. This represents a fundamental shift in how media companies view their content libraries, recognizing them as valuable AI training data beyond traditional monetization channels.
The real-world applications are diverse and compelling: reality show producers tracking contestants across multiple cameras with trained face detection; concert promoters generating social media clips from live feeds to drive viewership; news organizations rapidly translating content across languages and platforms; and contextual ad placement based on content sentiment analysis. Each implementation is tailored to specific industry needs rather than forcing one-size-fits-all solutions.
Looking ahead, the next frontier lies in applying AI to video distribution itself – optimizing just-in-time encoding, improving device detection, and predicting viewing patterns to reduce infrastructure costs while enhancing viewer experiences across devices and scenarios.

• Media Co-Pilot brings AI capabilities to complex streaming workflows while maintaining broadcast quality
• Creating private LLMs allows broadcasters to protect valuable content while leveraging AI capabilities
• Face detection and object recognition enable efficient reality show production across multiple cameras
• AI-powered scene analysis improves ad placement by understanding content context and sentiment
• Social media clip generation from live events happens automatically through AI processing
• Transcription, translation and dubbing workflows become more efficient through trained AI models
• Media archives represent valuable training data that companies should protect from unauthorized scraping
• Future applications will focus on using AI to optimize video distribution and just-in-time encoding
• Neural Content Processing servers enable faster training for customer-specific AI use cases
Ready to explore how AI can transform your media workflows? Connect with Ryan Jespersen to see Cires21's Media Co-Pilot demonstrations and discover the practical applications of AI in professional media production.

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Chapters

1. Introduction to Ryan Jesperson (00:00:00)

2. Media Co-Pilot: AI for Tier One Broadcasters (00:01:22)

3. Integrating AI with Existing Streaming Systems (00:05:08)

4. Unique AI Use Cases for Media Production (00:11:45)

5. The Future of AI in Video Distribution (00:18:40)

6. Content Value and LLM Training Models (00:29:05)

7. Selecting the Right AI Models for Specific Tasks (00:34:25)