This application transforms long-form reality TV episodes into a personalized vertical feed of short clips. Pegasus 1.5 extracts per-scene metadata; Jockey identifies season-defining moments, tracks cast relationships, and boosts matching clips; and Marengo 3.0 performs natural language search.

This application helps sports production teams turn a long game video into ready-to-use highlights. Marengo 3.0 finds the key moments, and Pegasus 1.5 describes each clip. Jockey then groups the clips into themed categories such as best plays and top reactions. It also answers questions about the game in plain language.

This application uses the semantic search capabilities of the platform to identify the most suitable influencers (organic brand fans) to reach out to.

This application simplifies the cross-platform video promotion workflow by generating unique posts for each social media platform.

This application uses image queries to find color shades in videos.

This application evaluates job interview performances using the ability of the Pegasus video understanding engine to generate text based on video content.

This application uses the Marengo video understanding engine to classify sports footage based on specific classes.

This application processes security footage, dash camera videos, and CCTV recordings to identify and timestamp key security events such as unauthorized access attempts or suspicious behavior patterns.

This application automatically creates multiple-choice questions from your video content, enabling educators and content creators to transform passive video viewing into interactive learning experiences.

This application allows you to search for specific video content using text or image queries. You can refine your visual searches in real-time by cropping any section of your query image.

This application automatically analyzes video content to create chapters and highlights, streamlining the video production workflow for content creators.

This application transcribes and translates video content in multiple languages, offering adjustable proficiency levels.

This multimodal RAG application offers personalized fashion recommendations based on both text and image queries. It utilizes the Embed API for analyzing video content, Milvus for vector searches, and GPT-3.5 for natural language processing.

This application analyzes source footage, summarizes content, and recommends ads based on the footage’s context and emotional tone. It also supports embedding-based searches and suggests optimal ad placements, letting you preview how the footage and ads fit together.

This application provides tailored video recommendations based on your profile and preferences, plus embedding-based searches for more accurate results.

This application combines TwelveLabs’ video embedding capabilities with Qdrant’s vector similarity search functionality. This integration enables semantic understanding of video content, allowing you to discover relevant videos based on meaning rather than simple keyword matching.