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Analyse efficiently, save downlinking costs

Superhuman Vision accurately detects critical targets and removes unusable data, before ever downlinking.

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Lightweight, on-premise solutions

With models as small as 5mb, our power efficient solutions are optimized for low-latency, low-data scenarios where every ounce of hardware is critical.

Trainable on limited data

Uses 5-10 images to learn new, specialized use cases

Simple installation

Easily integrates with existing hardware, supports SAR

Super secure

All sensitive data remains securely with clients.

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Make your satellites smarter with a lightweight, next-gen computer vision SDK.

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Video Services
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Moderate & manage video content with ease

Video Services
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Moderate & manage video content with ease

Asset Management
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Visual data management made easy

Asset Management
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Visual data management made easy

Creative Platforms
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Source the best visual content

Creative Platforms
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Source the best visual content

Press & Broadcasting
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Empower journalists with tagged visual archives

Press & Broadcasting
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Empower journalists with tagged visual archives

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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.