{"version":1,"type":"story","url":"https://digestai.news/story/conde-nast-cuts-video-search-time-from-250-minutes-to-2-minutes-using","json":"https://digestai.news/story/conde-nast-cuts-video-search-time-from-250-minutes-to-2-minutes-using.json","markdown":"https://digestai.news/story/conde-nast-cuts-video-search-time-from-250-minutes-to-2-minutes-using.md","slug":"conde-nast-cuts-video-search-time-from-250-minutes-to-2-minutes-using","headline":"Condé Nast cuts video search time from 250 minutes to 2 minutes using Amazon Bedrock and TwelveLabs Marengo","summary":"Condé Nast’s editorial teams spent an average of **250 minutes per task** manually searching through **140,000+ videos** using only titles and descriptions. This slowed content discovery for brands like *Vogue*, *GQ*, and *Wired*, hurting revenue capture in a fast-moving media environment. The bottleneck stemmed from traditional search tools’ inability to analyze video content beyond metadata.\n\nThe publisher partnered with **AWS’s Generative AI Innovation Center** to build a **multimodal video discovery system** using **Amazon Bedrock** and the **TwelveLabs Marengo embedding model**. The solution processes video transcripts, visuals, and audio into vector embeddings, enabling intent-based semantic search. By decoupling embedding generation from search serving, the team scaled the system efficiently. Results include a **99.2% reduction in discovery time** (from 250 minutes to **under 2 minutes**), a **90% drop in manual review effort**, and **$800,000 in annual savings**. The architecture’s high availability and modular design also ensure reliability for daily use.","keyPoints":["Condé Nast’s video search time dropped from 250 minutes to under 2 minutes per task using TwelveLabs Marengo embeddings on Amazon Bedrock","System combines video transcripts, audio, and visuals into vector embeddings for intent-based semantic search, reducing manual review by 90%","Estimated annual savings of $800,000 from productivity gains across 140,000+ videos, with multi-AZ design ensuring uptime"],"whyItMatters":"This case study demonstrates how multimodal AI can transform media workflows by replacing slow, manual video searches with semantic understanding. The decoupled architecture and cost savings offer a blueprint for industries managing large video archives, from broadcasters to enterprise media teams.","category":{"slug":"enterprise","name":"Enterprise & Industry","url":"https://digestai.news/category/enterprise"},"entities":{"companies":["Condé Nast","AWS","Amazon","TwelveLabs","AWS Generative AI Innovation Center"],"models":["TwelveLabs Marengo"],"people":["Billy Keenly"]},"firstPublishedAt":"2026-09-29T15:55:17Z","updatedAt":"2026-09-29T15:55:17Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"AWS Machine Learning Blog","title":"How Condé Nast built multimodal video discovery with Amazon Bedrock","url":"https://aws.amazon.com/blogs/machine-learning/how-conde-nast-built-multimodal-video-discovery-with-amazon-bedrock","publishedAt":"2026-09-29T15:55:17Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Condé Nast cuts video search time from 250 minutes to 2 minutes using Amazon Bedrock and TwelveLabs Marengo\", 29 September 2026, https://digestai.news/story/conde-nast-cuts-video-search-time-from-250-minutes-to-2-minutes-using","publisher":"Digest AI","title":"Condé Nast cuts video search time from 250 minutes to 2 minutes using Amazon Bedrock and TwelveLabs Marengo","datePublished":"2026-09-29T15:55:17Z","url":"https://digestai.news/story/conde-nast-cuts-video-search-time-from-250-minutes-to-2-minutes-using"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}