In a milestone moment for AI driven video intelligence, TwelveLabs has announced the general availability of Marengo 3.0, its most advanced video understanding model to date. Introduced during AWS re:Invent, the next generation model promises to redefine how enterprises analyze, interpret, and derive value from video content by providing richer contextual reasoning and more efficient performance than traditional machine learning approaches.
TwelveLabs, regarded as the leading innovator in video search and comprehension, describes Marengo 3.0 as a breakthrough foundation model that goes beyond frame by frame analysis. It processes audiovisual information with human like depth, capturing dialogue, sound, motion, emotional cues, and scene continuity in ways that previous technology could not. Unlike systems that rely on stitched together image and audio components, Marengo 3.0 interprets video as a coherent living system, capable of linking events and meaning across minutes rather than isolated frames.
The company emphasized that Marengo 3.0 is production ready and built for immediate deployment, with enterprise customers now able to access it through TwelveLabs directly or via Amazon Bedrock. Based on internal testing, the platform delivers a 50 percent reduction in storage costs and indexing speeds twice as fast as the previous generation. These efficiencies make it a viable choice for businesses with large video archives and demanding real time intelligence requirements.
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“Video represents 90 percent of digitized data, but that data has been largely unusable because it takes too long for humans to break down, and machines have been incapable of grasping and accounting for everything that happens in video,” said Jae Lee, CEO and co founder of TwelveLabs. “Solving this problem has been our singular obsession. Now, Marengo 3.0 shatters the limits of what is possible. It is an incomparable solution for enterprises and developers.”
The latest release focuses heavily on performance enhancements supporting industries including sports broadcasting, media and entertainment, advertising, public safety, and government. Key capabilities include temporal and spatial reasoning to connect actions and context over time, multimodal query support enabling users to combine text and images in a single search, and advanced team and player level tracking designed for high speed sports video analysis. The model supports up to four hours of video per file and provides multilingual recognition across 36 languages.
Nishant Mehta, VP of AI Infrastructure at AWS, underscored the significance of the collaboration. “TwelveLabs’ work in video understanding is transforming how entire industries manage their video capabilities, bringing unprecedented speed and efficiency to what has largely been a manual process,” Mehta said. “We are excited to be the first cloud provider to offer Marengo 3.0 to our customers through Amazon Bedrock, following great adoption from TwelveLabs previous Marengo and Pegasus models.”
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The availability of Marengo 3.0 through Amazon Bedrock enables secure, scalable integration inside existing AWS environments, allowing organizations to deploy advanced solutions without reengineering their infrastructure. It supports rapid experimentation and broad distribution within workflows such as content tagging, compliance monitoring, production automation, and complex investigative review.
For industries navigating increasing video volumes, the emergence of Marengo 3.0 signals a significant shift. As demand grows for automated analysis that mirrors human comprehension, the next wave of AI powered video solutions will be defined by contextual intelligence, multimodal reasoning, and platform level scalability.
Looking forward, the launch positions TwelveLabs at the center of a rapidly expanding ecosystem in which true video comprehension may become a critical differentiator for companies seeking deeper insight, operational efficiency, and competitive advantage. With its most advanced video understanding model now in market, the company is setting the stage for the next evolution in visual intelligence.
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