About the Company




years on the market

€6+ Million

funding raised


Protecting your machines and your business

We enable you to operate your machines more efficiently and sustainably. Our powerful AI and industrial IoT solutions analyze sound patterns, so potential mechanical failures can be detected early. We chose to specialize in sound processing because the sound analysis is a simple, universal, and reliable method of machine health diagnosis.

Look at our journey


  • founded by Pavel Konečný in Prague
  • accelerated by Startup Yard Prague
  • named “Idea of the Year 2016” in the Czech Republic for solution combining AI and IoT


  • € 600,000 investment by J&T Ventures
  • featured in Forbes Next
  • accelerated by Airbus BizLab in Hamburg


  • classified as “Cool Vendor in Acoustic Technologies for Predictive Maintenance” by Gartner
  • won 3rd place in the CEE startup competition PowerUp! by InnoEnergy
  • Accelerated by SAP Industry 4.0 Program in Berlin


  • € 5.75 million raised from investment funds – Inven Capital (ČEZ Group) and Lead Ventures (MOL Group)
  • joined the ESA BIC Center in Prague
  • awarded as the Best AI Startup in Czechia, the Best IoT Startup in Central Europe in the Central European Startup Awards Competition.

Latest news

How we predict machine failures with an audio and machine learning algorithms

Factory manufacturing is highly automated today. Yet wear and tear on the mechanical parts of the equipment can cause production to be suspended. Monitoring the condition of machines and predicting th

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Neuron soundware predictive maintenance protects gearbox production

Our goal in working with this recognised European automotive manufacturer was to keep gearbox production on target and avoid unplanned failures in the helium compressor, a critical production line com

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Prague public transport chooses Neuron soundware for predictive escalator maintenance

DPP, Prague’s public transport company, has begun working with Neuron soundware, a leading Czech tech firm in Industry 4.0, on predictive maintenance for escalators.  The project involves the conti

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