Empowering industry leaders

Our technology removes common barriers that stand in the way of innovation.

Process Modeling
Chemicals & Materials
Specialty Glass Production

Specialty glass manufacturer SCHOTT AG operates large tanks that melt glass at more than 1,600 °C. The melting process is governed by complex thermal and fluid dynamics that must be carefully controlled to transform raw materials into a homogenous mixture devoid of gaseous inclusions. By evaluating the large amounts of data generated through SCHOTT’s high-end sensor technology situated in the tanks, NNAISENSE neural networks provide completely new insights into the production process, helping tank operators to maintain optimal conditions – a perfect example of our value-add for industrial manufacturing.

Machinery & Engineering
Additive Manufacturing

Selective Laser Sintering (SLS) builds functional metal parts of almost unlimited geometric complexity using a powerful laser to melt layers of metallic powder onto previous layers, one 2D slice at a time. During this process, the distribution of laser energy within the layer is a key factor determining the material properties of the part. In partnership with EOS, the global innovation leader in industrial 3D printing, we have developed the first deep network model that accurately predicts this heat map based on job parameters in order to detect process anomalies when sensor readings deviate from predicted behavior, and control laser intensity to avoid defects and optimize material properties.

Renewables & Energy
Wind Turbine Inspection

Wind turbines have long been inspected by technicians climbing “up tower”, a harrowing and time-consuming endeavor. The Sulzer & Schmid 3DX platform makes this largely obsolete by using autonomous drones to efficiently capture consistent high-quality images of the blade surface. Prior to working with NNAISENSE, humans would pore over hundreds of images, manually marking areas of potential damage for experts to analyze. Now, with our custom deep network solution, this is done automatically with higher accuracy, reducing the time for this pre-annotation stage from over 1.5 hours per turbine down to 1 minute.

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