Explore key takeaways from our recent webinar on leveraging artificial intelligence to transform content workflows, boost discoverability, and unlock new monetization opportunities.
In a recent webinar, Vanessa Fiola from Qvest and Michael Kaplan from NVIDIA delved into "Unlocking the Value of Media Content," showcasing how artificial intelligence is evolving the media & entertainment industry. They explored how AI, particularly generative AI, is tackling the challenge of unlocking value from live feeds to vast media libraries, which are often difficult for media companies to access, monetize, or efficiently process.
The session highlighted the powerful synergy between NVIDIA's foundational AI technologies and Qvest's expertise in building practical, industry-specific solutions. Kaplan introduced key NVIDIA innovations that reduce development cycles–in some cases by 40%.
Qvest’s AI toolkits and accelerators, designed to fast-track production, illustrated real-time video analysis – including object detection, alerts, and brand recognition – all managed via a no-code, natural language agent builder. See Qvest AI accelerators overview. A live demo featured their multi-agent no-code accelerator using NVIDIA Holoscan for Media (a platform for real-time, low-latency AI processing of live media). Both demonstrated scalable AI frameworks, with Qvest discussing their Model Context Protocol (MCP) approach to AI systems and applications. NVIDIA highlighted its AI-Q Blueprint and Agent Intelligence Toolkit for developing sophisticated AI agents.
To gain a comprehensive understanding of how Qvest and NVIDIA are enabling media and content organizations to harness the power of AI, you can watch a preview below, or view the full webinar on demand here.
Their conversation spanned the building of applications to optimizing for long-term cost considerations to driving to AI adoption from day one.
Moving from proof of concept where the focus is on validating predictability to production (which requires optimizing for speed, cost, and scalability). Qvest shared a case study where switching to an optimized model on NVIDIA GPU led to an ~80% compute cost saving and significant ROI.
Fiola detailed Qvest's proprietary model for identifying high-value AI use cases, balancing business value, user adoption readiness, data readiness, and technology maturity as indicators for successful implementation.
A standout example was Qvest's AI-driven service that automates marketing assets creation from Photoshop files, drastically reducing manual asset versioning workloads for a client.
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