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TwelveLabs to Bring Its State-of-the-Art Video AI Models to Amazon Bedrock

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Latest collaboration enables developers to leverage TwelveLabs’ video understanding capabilities to create new applications with the security, privacy, and performance of AWS

LAS VEGAS, April 6, 2025 /PRNewswire/ — Amazon Web Services (AWS), an Amazon.com, Inc. company and TwelveLabs, the video understanding company, today announced that TwelveLabs’ state-of-the-art multimodal foundation models, Marengo and Pegasus, will soon be available in Amazon Bedrock. Amazon Bedrock is a fully managed service that offers developers access to high-performing models from leading AI companies through a single API. Seamless access to TwelveLabs’ advanced video understanding capabilities will enable developers and enterprises to transform how they search, analyze, and generate insights from video content, leveraging the security, privacy, and performance of AWS. AWS is the first cloud provider to offer models from TwelveLabs.

“This integration with Amazon Bedrock represents the next phase in our collaboration with AWS, making our video understanding AI more accessible to enterprises worldwide,” added Lee.

“Video contains nearly 80% of the world’s data, yet most of it remains unsearchable and underutilized,” said Jae Lee, Co-founder and CEO of TwelveLabs. “By making our models available through Amazon Bedrock, we’re empowering even more enterprises to bring video understanding to their existing infrastructure. Our technology enables users to search across their entire content library—from videos collected 10 years ago or 10 minutes ago—to find the precise moment they’re looking for in less than a single second, and then interpret and analyze those moments. This opens the door for all kinds of novel uses. Through the collaboration with AWS, we can extend powerful capabilities to customers and accelerate innovation across industries.”

Advanced AI Video Capabilities

Video is commonly regarded as one of the world’s largest unsearchable data sources, yet TwelveLabs’ cutting-edge technology turns it into a trove of accessible information. Whether it’s giving a sports network the ability to instantly pull every instance of a specific play style or commentator reaction or helping a broadcaster identify recurring themes across large volumes of footage, TwelveLabs helps teams turn their video archives into usable, indexable assets, unlocking both operational efficiency and new revenue opportunities.

TwelveLabs overcomes the inherent complexities associated with video understanding to allow customers to search video across all modalities. Specifically, TwelveLabs delivers:

Natural language video search that pinpoints precise content momentsDeep video understanding without requiring pre-defined labelsMultimodal AI processing visual, audio, and text simultaneouslyTemporal intelligence connecting related events across timeEnterprise solutions scaling extensive video libraries into accessible knowledge

“At MLSE, we are defining the future of the sports and entertainment business. Innovation is in our DNA, and we’re leading the charge in shaping what comes next. With powerful tools like Amazon Bedrock and TwelveLabs’ AI models supporting our vision, we’re accelerating our ability to create smarter, more immersive experiences for fans.” said Humza Teherany, Chief Strategy and Innovation Officer at Maple Leaf Sports & Entertainment.”

Unlocking the Power of Video Understanding for AWS Customers

With Marengo and Pegasus available in Amazon Bedrock, AWS customers can use TwelveLabs’ models to build and scale generative AI applications without managing underlying infrastructure. Using Amazon Bedrock, customers gain access to a broad set of capabilities while maintaining complete control over their data, benefiting from enterprise-grade security and utilizing cost control features—all essential for deploying AI responsibly at scale.

TwelveLabs’ fully managed, serverless models in Amazon Bedrock allow developers to:

Create applications that search through videos, classify scenes, summarize content, and extract insights using natural languageBuild sophisticated video understanding features without specialized AI expertiseScale video processing from small collections to massive libraries with consistent performanceDeploy solutions with enterprise-grade security and governance controls

“Video understanding is revolutionizing how industries like media & entertainment, sports, automotive, and education work with and discover content,” said Samira Panah Bakhtiar, General Manager of Media & Entertainment, Games, and Sports at AWS. “Over the last year, I have consistently said that natural language semantic search is a ‘strategic unlock’ for our entertainment customers, as they reexamine their existing intellectual property and breathe new life into it. By bringing TwelveLabs’ advanced models to Amazon Bedrock, we’re helping our customers make sense of any video moment, unlocking the full value of their treasured video assets. Businesses will now be able to easily search, categorize, and extract insights from their vast video libraries, enabling new use cases and better user experiences that were previously impossible without significant technical expertise.”

The integration will benefit multiple industries, from media, entertainment, advertising and beyond. For example:

Film and TV Studios can rapidly manage video workloads from dailies, content repacking, and archive managementSports Leagues and Teams can efficiently create match highlights and create customized fan focused content at scaleNews Agencies and Broadcasters can quickly manage large libraries to find the moments that matterStreaming services can better package and distribute content across platforms and more effectively insert relevant video ads

AWS and TwelveLabs’ integration partner Monks expressed their excitement: “We’ve been putting AI to work across the entire video value chain for IP holders, broadcasters and brands. TwelveLabs in Amazon Bedrock makes it easier to realize opportunities for monetization in broadcast news, entertainment and sports by making it simpler and more secure to build and scale applications with powerful video understanding,” said Lewis Smithingham, EVP Strategic Industries at Monks.

Expanding Collaboration Between AWS and TwelveLabs

This announcement builds on a strong existing relationship between AWS and TwelveLabs and continues the momentum of their Strategic Collaboration Agreement (SCA). TwelveLabs is working with AWS to accelerate the development of its foundation models, deploy its advanced video understanding foundation models across new industries, and enhance its model training capabilities using Amazon SageMaker HyperPod. With the reliable and scalable infrastructure offered by SageMaker HyperPod, TwelveLabs has accelerated model training while reducing training costs.

“This integration with Amazon Bedrock represents the next phase in our collaboration with AWS, making our video understanding AI more accessible to enterprises worldwide,” added Lee.

To learn about TwelveLabs’ industry leading models, please explore twelvelabs.io, Marengo 2.7 and Pegasus 1.2. Find out more about TwelveLabs models in Amazon Bedrock here.

About TwelveLabs

TwelveLabs uses multimodal foundation models to bring human-like understanding to video data. The company’s foundation models map natural language to what’s happening inside a video, including actions, objects, and background sounds, allowing developers to create applications that can search through videos, classify scenes, summarize, and extract insights with unprecedented accuracy. Headquartered in the US, TwelveLabs serves customers across media, entertainment, sports, advertising, and government. For more information, visit www.twelvelabs.io

Media Contact

Amber Moore, Moore Communications, 1 5039439381, amber@moorecom2.com, Moore Communications

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Chef Robotics Physical AI Models Can Now Automate Baked Goods Packing

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SAN FRANCISCO, April 29, 2026 /PRNewswire/ — Chef Robotics, a leader in physical AI for the food industry, today announced that Chef robots can now automate tray assembly for baked goods packing. The application places baked products, such as burger buns, chocolate chip cookies, biscotti, butter cookies, biscuits, fortune cookies, granola bars, rusks, and shortbreads into trays and packaging containers before sealing.

Watch Chef robots in action.

Baked goods packing has historically been difficult to automate for high-mix production. Each item behaves differently on the production line—a granola bar compresses under the wrong grip, while a biscotti or rusk can crack if placed at the wrong angle. Surface textures range from glazed and smooth to crumbly and irregular, and strict presentation requirements leave little room for error. This variability has made it challenging for automation systems to reliably handle baked goods at production speeds, leaving food manufacturers dependent on manual labor and traditional bakery equipment.

To address this, Chef built its baked goods packing application on its existing piece-picking capability, which uses Chef’s AI-powered computer vision and physical AI models trained across diverse real-world production environments. This allows Chef robots to assess each item’s position, shape, and orientation in real time and determine how to pick the items from the pan and place them quickly and precisely without damaging them.

The baked goods packing application supports four distinct placement capabilities.

First, Chef’s vision system detects the angle at which each item sits in the pan and reorients it after picking, placing it on the tray at the exact angle required, regardless of its original position, enabling retail-ready presentation for SKUs that require precise angular placement.

Second, Chef robots can place multiple baked goods into the same packaging container in a single automated pass, completing full tray assembly without manual intervention.

Third, for packaging containers with multiple small compartments, Chef robots can precisely place items into each designated section, including multiple items in the same compartment, using Chef’s AI vision model to detect compartment positions and orientations in real time.

Fourth, Chef’s vision system identifies the exact center of each tray and places every item at a predefined offset from that center, ensuring a uniform, consistent arrangement across every pack regardless of how trays arrive on the conveyor.

For food manufacturers evaluating bakery systems and baked goods packaging automation, the application offers higher throughput, reduced labor dependency, and consistent presentation across shifts. The capability runs on Chef’s existing robotic hardware and software, allowing manufacturers to deploy it without requiring any changes to their production lines.

Chef’s baked goods packing application is available in the U.S., Canada, Germany, and the UK and is included as part of Chef’s robotics-as-a-service (RaaS) pricing model.

About Chef Robotics
Chef is the first company to have commercialized a scalable AI-driven food robotics solution. With over 104 million servings made in production, Chef leverages ChefOS, an AI platform for food manipulation, to offer a Robotics-as-a-Service solution that helps industry-leading food companies increase production volume and meet demand. Headquartered in San Francisco, CA, Chef aims to empower humans to do what humans do best by accelerating the advent of intelligent machines. Visit https://chefrobotics.ai to learn more.

View original content:https://www.prnewswire.com/news-releases/chef-robotics-physical-ai-models-can-now-automate-baked-goods-packing-302756923.html

SOURCE Chef Robotics

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Chef Robotics Physical AI Models Can Now Automate Baked Goods Packing

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SAN FRANCISCO, April 29, 2026 /PRNewswire/ — Chef Robotics, a leader in physical AI for the food industry, today announced that Chef robots can now automate tray assembly for baked goods packing. The application places baked products, such as burger buns, chocolate chip cookies, biscotti, butter cookies, biscuits, fortune cookies, granola bars, rusks, and shortbreads into trays and packaging containers before sealing.

Watch Chef robots in action.

Baked goods packing has historically been difficult to automate for high-mix production. Each item behaves differently on the production line—a granola bar compresses under the wrong grip, while a biscotti or rusk can crack if placed at the wrong angle. Surface textures range from glazed and smooth to crumbly and irregular, and strict presentation requirements leave little room for error. This variability has made it challenging for automation systems to reliably handle baked goods at production speeds, leaving food manufacturers dependent on manual labor and traditional bakery equipment.

To address this, Chef built its baked goods packing application on its existing piece-picking capability, which uses Chef’s AI-powered computer vision and physical AI models trained across diverse real-world production environments. This allows Chef robots to assess each item’s position, shape, and orientation in real time and determine how to pick the items from the pan and place them quickly and precisely without damaging them.

The baked goods packing application supports four distinct placement capabilities.

First, Chef’s vision system detects the angle at which each item sits in the pan and reorients it after picking, placing it on the tray at the exact angle required, regardless of its original position, enabling retail-ready presentation for SKUs that require precise angular placement.

Second, Chef robots can place multiple baked goods into the same packaging container in a single automated pass, completing full tray assembly without manual intervention.

Third, for packaging containers with multiple small compartments, Chef robots can precisely place items into each designated section, including multiple items in the same compartment, using Chef’s AI vision model to detect compartment positions and orientations in real time.

Fourth, Chef’s vision system identifies the exact center of each tray and places every item at a predefined offset from that center, ensuring a uniform, consistent arrangement across every pack regardless of how trays arrive on the conveyor.

For food manufacturers evaluating bakery systems and baked goods packaging automation, the application offers higher throughput, reduced labor dependency, and consistent presentation across shifts. The capability runs on Chef’s existing robotic hardware and software, allowing manufacturers to deploy it without requiring any changes to their production lines.

Chef’s baked goods packing application is available in the U.S., Canada, Germany, and the UK and is included as part of Chef’s robotics-as-a-service (RaaS) pricing model.

About Chef Robotics
Chef is the first company to have commercialized a scalable AI-driven food robotics solution. With over 104 million servings made in production, Chef leverages ChefOS, an AI platform for food manipulation, to offer a Robotics-as-a-Service solution that helps industry-leading food companies increase production volume and meet demand. Headquartered in San Francisco, CA, Chef aims to empower humans to do what humans do best by accelerating the advent of intelligent machines. Visit https://chefrobotics.ai to learn more.

View original content:https://www.prnewswire.com/news-releases/chef-robotics-physical-ai-models-can-now-automate-baked-goods-packing-302756923.html

SOURCE Chef Robotics

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Air Products to Expand Industrial Gas Supply for Samsung Electronics’ Next-Generation Semiconductor Fab in South Korea

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New investment underscores the company’s long-term commitment to Korea and its leading role in the global semiconductor industry 

LEHIGH VALLEY, Pa., April 29, 2026 /PRNewswire/ — Air Products (NYSE:APD), a world-leading industrial gases company and serving Samsung globally, today announced it has been selected by Samsung to supply industrial gases for its new advanced semiconductor fab in Pyeongtaek, Gyeonggi Province, South Korea.

Under the agreement, Air Products will build, own and operate multiple state-of-the-art production facilities and a bulk specialty gas supply system to supply nitrogen, oxygen, argon, and hydrogen for Samsung’s new semiconductor fab. The new facilities are expected to come onstream in multiple phases from 2028 through 2030.

Air Products has a long track record of executing multiple phase expansions in Pyeongtaek to support Samsung’s growing manufacturing needs. This latest project represents Air Products’ largest investment to date in the semiconductor industry and will establish Pyeongtaek as the company’s single largest operations site globally supporting the electronics industry. 

“Air Products is honored to be selected once again by Samsung and to have their continued confidence as a trusted partner supporting their strategic growth plans,” said SR Kim, President, Air Products Korea. “This significant investment reinforces Air Products’ role as a leading global supplier to the semiconductor industry and underscores our long-standing commitment to supporting our strategic customers with safety, reliability, efficiency and excellent service.”

Air Products has served the global electronics industry for more than 40 years, supplying industrial gases safely and reliably to many of the world’s leading technology companies. The company has operated in Korea for more than 50 years and has established a strong position in electronics and manufacturing sectors.

About Air Products

Air Products (NYSE: APD) is a world-leading industrial gases company in operation for over 85 years focused on serving energy, environmental, and emerging markets and generating a cleaner future. The Company supplies essential industrial gases, related equipment and applications expertise to customers in dozens of industries, including refining, chemicals, metals, electronics, manufacturing, medical and food. As the leading global supplier of hydrogen, Air Products also develops, engineers, builds, owns and operates some of the world’s largest clean hydrogen projects, supporting the transition to low- and zero-carbon energy in the industrial and heavy-duty transportation sectors. Through its sale of equipment businesses, the Company also provides turbomachinery, membrane systems and cryogenic containers globally.

Air Products had fiscal 2025 sales of $12 billion from operations in approximately 50 countries. For more information, visit airproducts.com or follow us on LinkedInXFacebook or Instagram.

This release contains “forward-looking statements” within the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. These forward-looking statements are based on management’s expectations and assumptions as of the date of this release and are not guarantees of future performance. While forward-looking statements are made in good faith and based on assumptions, expectations and projections that management believes are reasonable based on currently available information, actual performance and financial results may differ materially from projections and estimates expressed in the forward-looking statements because of many factors, including the risk factors described in our Annual Report on Form 10-K for the fiscal year ended September 30, 2025 and other factors disclosed in our filings with the Securities and Exchange Commission. Except as required by law, we disclaim any obligation or undertaking to update or revise any forward-looking statements contained herein to reflect any change in the assumptions, beliefs or expectations or any change in events, conditions or circumstances upon which any such forward-looking statements are based.

View original content to download multimedia:https://www.prnewswire.com/news-releases/air-products-to-expand-industrial-gas-supply-for-samsung-electronics-next-generation-semiconductor-fab-in-south-korea-302757497.html

SOURCE Air Products

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