Due to advances in the field of machine learning in recent years, any pattern in image data visible to the human eye can also be made visible to a machine. Sometimes machines can even be trained to detect structures not visible to humans.
Currently the process is cost-intensive and/or a faster decision creates substantial value
A (trained) human could make a good decision mainly based on visual information
There is enough data available (as a rule of thumb: 200 - 2.000 images. This, of course, is highly dependent on the use-case)
In our experience, only by combining knowhow of internal operations with machine vision expertise, projects can be framed well. Feel free to approach us with questions, especially whether we deem your project to be technically feasible.
Together we discuss your process automation projects along three different dimensions: cost savings, strategic value and technical feasibility. After settling for a specific project, we put special emphasis on the needs of the end users.
We are an experienced team of machine learners. Our algorithms find complicated patterns in unstructured, mostly visual and text data. Once detected, these patterns are the basis for the automation of the underlying process.
We make a point of integrating our customers in the project's code repository as well as in weekly progress meetings. Agility, clean code and a modular program structure help us to deliver easy-to-maintain software, that simply works.
Creative solutions enabled us to automate the process of planning solar systems.
See how machine learning can be used to stop environmental destruction.
AI-supported optical defect detection for semiconductor laser production.
We automated the detection of certain cloud structures for Deutscher Wetterdienst (DWD).
An image segmentation algorithm that supports sustainable city planning.
Machine Learning Project Lead
Machine Learning Consultant
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