We use AI methods to understand audio signals


We developed a diagnostic technology for early detection of broken machines. Humans are frequently used during the quality controls of air-conditioning, gearbox, etc. We can automate the analysis using our digital brain, which emulates auditory cortex. We focus on the early detection and prediction of mechanical malfunction on any machinery. The unique pre-processing of the input data allows the neural network to learn and identify the important features quickly with high confidence.



We can identify the mechanical issues using a few seconds of the sound with high accuracy.



We have processed terabytes of training audio data for our clients.



We are proud that customers stay with us. Unfortunately their lawyers like NDA.

We are bringing innovations in the areas of:



We monitor the sounds and other parameters of the machine and use probabilistic methods to determine the risk of an outage of each individual device. That’s the revolution of the predictive maintenance – individualized monitoring.



Innovative digitization and analytic platform that enables manufacturers to check their critical machinery. The integrated hardware and software platform automatically gathers data in real time and continuously assess the equipment health.



The lack of a preventive maintenance increases the risk of costly unplanned downtime. The existing equipment doesn’t provide effective monitoring of mechanical malfunctions. We believe sound analysis is the simplest method of malfunction diagnostics.

You’re looking at our newest model: nBox

How does our

magic work

Sound Recording

Our Neuron box is monitoring and recording the sounds and other parameters of the machine

Neural Network

Passing all data trough tailored deep neural network to determine the risk of an outage of each individual device

Problem detection

We detect unusual or anomalous behaviors in real-time, vizualize them and send notification to responsible person

Still confused? Check our video below

Highly experienced and talented team

Our team consists of dedicated experts in IoT, Machine learning and also with experiences from the area of genetic research on developing a unique way of processing the sound. We spent years of work and hours of computational power to develop our technology.

We are continuously improving our algorithm and working with our clients to improve the revolutionary predictive maintenance of mechanical malfunction. Make it individual, real-time and perfectly accurate.

Latest news

Hello Space! Neuron soundware has joined the ESA BIC Centre in Prague

On June 17, we kicked off our already third significant interaction with the aerospace industry in our three year company history. The European Space Agency’s Business Incubation Centre (ESA BIC) in Prague has selected Neuron soundware among five new companies to join...

“Machine Whisperer” Neuron soundware presents three world firsts at Hannover Messe

Hannover / Prague, 07 March 2019: The "machine whisperers" from Neuron soundware are back in time for Hannover Messe with three new products that they will be presenting at the world's most important industrial fair. After Neuron soundware presented its own AI-based...

nBox – The IoT recording & edge computing device

This standalone IoT recording & computing device can be installed to enable audio monitoring of any asset. Discover the abundant edge computing power that will give you the possibility to run complex machine learning models without a need to transfer data to cloud....

A New Microsite for Cogeneration Unit Predictive Maintenance Solution

We have monitored many cogeneration units with our technology in 2018. It makes a perfect sense for us to focus on this market even more as some of the current customers have acknowledged that on several failure occasions our solution helped them to:   save several...

nCard, The Mobile Recording Gadget, Is Available Now

Neuron soundware is happy to introduce a new piece of HW, the nCard, a custom-designed shielded sound card that connects to a mobile phone and allows to make high-resolution audio recordings anytime, anywhere. Recording of broken machines has never been easier. We are...

Work & Travel Greece: The Neuron Soundware Experience

The “GREAT” idea If you are a startup you get plenty of possibilities how to enjoy work life in a somewhat different fashion. Getting away from daily office routine is certainly refreshing and motivating for any kind of team, and so it happened one day that our CEO...

Neuron Soundware Selected for SAP Industry 4.0 Startup Program

The startup, which recognizes machine problems by their sound, prevailed among 75 applicants The 6-week startup program will be concluded with a demo day on July 5 at SAP headquarters in Walldorf - representatives of the press are cordially invited...

Achieving Super-Human Maintenance Capabilities

Pavel Konecny, CEO of Neuron soundware, discusses his solution to predictive maintenance to further the efficiency of machinery as we enter Industry 4.0 Over the last two years, I have been working on analysing audio signals obtained from heavy machinery, engines,...

Neuron soundware classified as a 2018 “Cool Vendor in Acoustic Technologies for Predictive Maintenance” by Gartner

The Gartner report identifies "Cool Vendors" in key technologies, recognizing innovative vendors, their products and services. Prague, May 8th 2018: Neuron soundware was recognized in the April 2018 Gartner Cool Vendors Report for Acoustic Technologies for Predictive...

Check Out Our New Product Demo Unit

For our potential customers, to make it easier for them to understand how our technology works, we designed a brand new product demo unit. Browse the slideshow for more pictures...   Made from two small electric engines (one okay, the other...

Let’s get in touch


71-75 Shelton Street, Covent Garden

Our office

Bubenské nábřeží 306/13, Building 24 – Prague Startup Market
170 00 Prague, Czech Republic

pavel.konecny@neuronsw.com | +420 604 182 351

Head of Sales

jiri.cermak@neuronsw.com | +420 603 884 011

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This project has received funding from the European Union`s Horizon 2020 research and innovation programme under grant agreement No 808225.
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