Beyond the Frame: Multimodal Video Recommendations with TwelveLabs + Qdrant

​Qdrant AI Builders: Video Recommendations with Twelve Labs

​
What if your app could understand what’s happening in a video — not just the title or transcript, but the emotion in the scene, the objects on screen, and the context of the conversation?

​Join Qdrant and Twelve Labs for a live, behind-the-scenes presentation on building smarter video recommendation systems using state-of-the-art vector search and multimodal AI. We’ll walk through a real open-source demo that combines Twelve Labs’ video intelligence API with Qdrant’s vector database to enable rich, semantic recommendations based on what’s actually happening inside the video.

​You’ll learn:

• ​How Marengo and Pegasus foundation models are transforming video understanding

• ​​Explore real-world applications in sports, media, and security solutions

• ​How multimodal embeddings work across audio, visual, and textual signals

• ​How to store and search them at scale using Qdrant

• ​What it takes to build a recommendation engine that feels intelligent

• ​Key takeaways from the GitHub project

​This event is for developers, ML engineers, product builders, and anyone curious about the next generation of video search and personalization.