Edge AI Platform

Your embedded AI - without any Expert Knowledge

20250226_LV_EdgeAIPlattform_Grafik — Illustration of how our Edge AI platform works.

Imagine a world where you can develop your own AI models directly for your products. Many customers are increasingly relying on AI capabilities in their products, and with our Edge AI Platform, we offer you the ability to quickly create powerful, embedded AI models and seamlessly integrate them into your systems. Harness the future of artificial intelligence! Our web application enables you to develop, evaluate and successfully deploy AI pipelines independently - become a pioneer in your industry!

Developing & deploying with the Edge AI Platform

Illustration of the process to demonstrate the time savings achieved by the Edge AI Platform

The Edge AI Platform accelerates the entire development process for embedded AI - from the task definition to the finished model. While conventional workflows require many manual steps and specialized expertise, our platform reduces the effort to the essentials: you define your task, upload your data - the platform takes care of the rest.

Your Advantages

Fast Proof of Concept

With the Edge AI Platform, you can evaluate the AI capabilities of your product in the shortest possible time. Create prototypes and Proof of Concepts (PoCs) faster than ever before to develop innovative solutions and test your ideas quickly.

Short Development Time

Minimize time to market with the Edge AI Platform. Thanks to the short development time, you can react more quickly to market requirements and make your products available promptly.

Making AI Simple

Our platform makes it possible to create AI models without any prior knowledge of embedded AI development. You can start immediately without having to learn complex technologies. Simple, fast and efficient!

What do you get?

Our Edge AI Platform provides you with a user-friendly interface to turn your AI ideas into reality. Here's what you can expect:

Input:

Data set provision: Upload your data to train the AI models.
Application parameters: Define your specific requirements and goals for the AI model.

Output:

Software library: Export the optimised AI models as a software library.
Performance reports: Detailed reports on AI performance, memory requirements.
Microcontroller recommendations: Use recommendations for suitable microcontrollers for effective implementation.