TwelveLabs Earns AWS AI Competency

For years, organizations have been sitting on petabytes of video they couldn’t search, analyze, or monetize because the tools necessary to understand it at scale didn’t exist. That's the problem TwelveLabs was built to solve. Amazon Web Services (AWS) gave us the infrastructure and distribution to do it at enterprise scale.

Today, we're proud to share that we’ve earned the AWS AI Competency, a designation that reflects both technical validation and real enterprise deployments on AWS infrastructure.

What the AWS AI Competency Actually Means

The AWS AI Competency isn't just a partner directory listing. AWS awards it after a rigorous evaluation process that requires demonstrated technical depth and documented customer outcomes. For enterprises evaluating AI vendors, it's one of the clearest signals that a partner has moved past promises and into proof.

TwelveLabs earned the AWS AI Competency based on the enterprise deployment and technical capabilities of our video understanding models, Marengo and Pegasus, both available today on Amazon Bedrock.

What Our Models Do

Marengo enables multimodal video search and semantic understanding across speech, motion, objects, scenes, and contextual relationships. It doesn't treat video as a collection of frames. It encodes it as a multidimensional block of data that can be searched and navigated at scale.

Pegasus transforms video into structured intelligence that supports summarization, reasoning, and downstream AI workflows. Where Marengo encodes, Pegasus reasons — identifying what happened, when, and why it matters.

Together, they give enterprises a complete pipeline: from raw video to searchable, indexable, actionable intelligence. Maple Leaf Sports & Entertainment put that pipeline to work across their franchises and were able to cut video search and retrieval from 16 hours down to 9 minutes, freeing their creative team to focus on fan-driven content instead of digging through archives.

A Deeper Partnership Than a Badge

The competency reflects a relationship that runs well past product listings. TwelveLabs CEO Jae Lee sits on the AWS Customer Advisory Board, providing direct input to AWS leadership on where video intelligence needs to go. We maintain active product collaboration with the Amazon S3 and S3 Vectors service teams.

Most partner relationships live on a webpage. This one shows up in the Bedrock catalog and on the AWS product roadmap.

The Archive Monetization Program

One of the most concrete outcomes of this collaboration is a program we co-developed along with AWS, Iconik, and Cloudfirst. Media companies get a funded, end-to-end path to migrate petabyte-scale video archives to Amazon S3, then use TwelveLabs to index that content for search, licensing, and monetization.

The results have been immediate for early participants. One major broadcast news company increased metadata coverage by 10x, opening up licensing pathways that had been blocked for years by the sheer inconsistency of legacy archive data.

To hear more about the program from the people who built it, you can register here for the webinar.

Start Building

If you're evaluating how to operationalize video intelligence workflows on AWS for media, enterprise search, compliance, or content monetization, Marengo 3.0 and Pegasus 1.2 are available today on Amazon Bedrock, with Pegasus 1.5 coming soon. The AWS AI Competency means that enterprises don't have to be the ones figuring out whether the stack is production-ready; AWS has already done the technical due diligence.