Industrial strength solutions driven by cutting-edge AI

The formidable technical challenges specific to most industrial use cases mean that off-the-shelf AI solutions are often too generic, resulting in lost time and investment. Our team is at the forefront of the rapidly expanding AI frontier, advancing the state-of-the-art and continually folding this know-how into the NNAI engine which we customize for three application areas: inspection, modeling and control.

Use cases:

  • Anomaly detection
  • Defect detection / classification
  • Continuous process monitoring

Inspection

Automated inspection is key to ensuring efficient quality control. Whether acquired through cameras, hyperspectral sensors or other metrology hardware, large amounts of data must be analyzed in real-time to avoid production delays. Deep Learning provides better accuracy by learning what features matter directly from your data, and can be cheaply deployed with high throughput on conventional hardware.

Use cases:

  • Anomaly detection
  • Defect detection / classification
  • Continuous process monitoring

Use cases:

  • Provide deep insights
  • Digital twinning
  • Predictive maintenance

Modeling

Predicting the dynamics of a process is the essential first step toward its optimization. Unfortunately, most modern industrial processes are simply too complex to be fully captured by conventional simulation approaches, leaving potentially critical details un-modeled. Our approach is to let the process speak for itself: data sampled from the actual process dynamics is used to learn a predictive model that is bespoke to that application.

Use cases:

  • Provide deep insights
  • Digital twinning
  • Predictive maintenance

Use cases:

  • Process optimization
  • Energy & waste reduction
  • Quality & yield improvement

Control

Intelligent automation means closing the sensory-motor loop in a way that goes beyond traditional control engineering. In order to fully unlock the hidden potential for increased productivity, controllers must be learned instead of programmed. This means applying Deep Reinforcement Learning to adapt neural network controllers through safe and efficient interaction with a learned process model.

Use cases:

  • Process optimization
  • Energy & waste reduction
  • Quality & yield improvement

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Lugano
Piazza Molino Nuovo 17
6900, Switzerland
Austin
1224 East 12th St., suite 313 Texas, 78702, USA
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