TwelveLabs

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TwelveLabs has 2 source-linked AI deployments documented in AIUseCaseHub, across 1 industry and 1 country.

Use Cases

Industries

Countries

Hyperscaler mix

See whether TwelveLabs's cases are powered by Microsoft, AWS, GCP, or multiple providers.

How TwelveLabs builds AIBuild, Buy & Compose — what they mean
Build — Custom, pro-code engineering — bespoke pipelines and SDKs (e.g. Bedrock, Vertex AI).
Buy — An off-the-shelf assistant or copilot, adopted as the main solution.
Compose — Low-code / no-code assembly (e.g. Copilot Studio, Power Platform, n8n).
Mixed — A combination of more than one of the above.

Build / Buy / Compose across this company's documented cases

BuildBuyComposeMixed

2 of 2 cases classified (100%) · Compare all use-case types

Use case portfolio

Use case types at TwelveLabs

Content discovery assistant leads with 1 of 2 documented cases; 2 distinct types appear across the visible portfolio.

Ranked by documented casesShare of visible cases

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Technology snapshot

What TwelveLabs uses across visible cases

Computer Vision appears in 1 of 2 indexed cases; 9 named technologies are mentioned, led by Amazon Bedrock.

Capability mix

Computer Vision50%

Technologies mentioned

Amazon Bedrock2 Amazon S32 Amazon S3 Vectors2 Amazon SageMaker HyperPod2 Amazon EC21 Amazon EC2 G6e Instances1 Amazon EC2 P5 Instances1 Amazon EKS1 Amazon Elastic Kubernetes Service1

All Use Cases (2)

TwelveLabs builds multimodal video understanding using Amazon Bedrock and SageMaker HyperPod

TwelveLabs developed multimodal video foundation models and deployed them on AWS to support training and high-volume inference at global production scale.Its Marengo and Pegasus models power semantic video search, summarization, and structured outputs over massive archives.

Tech & CommsJul 2026

TwelveLabs Unlocks the Full Potential of Video with Amazon Bedrock

TwelveLabs developed breakthrough multimodal video AI foundation models, Marengo and Pegasus, that understand video as unified stories across sight, sound, and time, enabling powerful video search and analysis.To deploy these models at production scale, TwelveLabs leveraged AWS infrastructure, including Amazon Bedrock, Amazon SageMaker HyperPod for model training resilience, Amazon EKS for scalable model serving, EC2 G6e and P5 instances for inference, and Amazon S3 with S3 Vectors for unified storage and vector-based semantic search.This unified AWS platform supports petabyte-scale video indexing and sub-second search across billions of vector embeddings, delivering reliable, cost-efficient video intelligence at unprecedented scale.TwelveLabs' solution transforms massive unstructured video archives into searchable, analyzable assets, powering applications across media, advertising, manufacturing, and government sectors.

Tech & CommsApr 2026

Vision

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