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Empower journalists with tagged visual archives

Superhuman Vision's automatic, in-depth image tagging lets journalists focus more on reporting.

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Press & Broadcasting

Analyzes everything

Super-quick and accurate tagging of visuals with objects, faces, concepts. Even identifies and tags abstract emotional content of a photograph.

Tags in milliseconds

Automatic tagging while importing to your archive

Recognizes emotions

Identifies more than just objects and people

Runs locally

An SDK which seamlessly integrates with the DAM platform

Hear it from our customers

With 100 million+ images and thousands of daily submissions, ANP journalists were struggling to manage their content archive. Our AI eased this process.

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Press & Broadcasting

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Press & Broadcasting
Stock Photography
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Easily identify relevant visuals for purchase

Stock Photography
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Easily identify relevant visuals for purchase

Video Services
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Moderate & manage video content with ease

Video Services
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Moderate & manage video content with ease

Printing Services
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Next-gen, AI-powered customer experience

Printing Services
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Next-gen, AI-powered customer experience

Space
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Analyse efficiently, save downlinking costs

Space
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Analyse efficiently, save downlinking costs

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Metadata Mastery

FREE training from the experts at Mobius Labs

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Dominic Rüfenacht - Head of Science | Co-founder bei Mobius Labs GmbH
Mobius Labs GmbH is receiving additional funding by the ProFIT program of the Investment Bank of Berlin. The goal of the ProFIT project “Superhuman Vision 2.0 for every application- no code, customizable, on- premise  AI solutions ” is to revolutionize the work with technical images. (f.e.) This project is co-financed by the European Fund for Regional Development (EFRE).

In the ProFIT project, we are exploring models that can recognize various objects and keywords in images and can also detect and segment these objects into specific pixel locations. Furthermore, we are investigating the application of ML algorithms on edge devices, including satellites, mobile phones, and tablets. Additionally, we will explore the combination of multiple modalities, such as audio embedded in videos and the language extracted from that audio.