Why data safety is a competitive edge in AI

In recent years, with the growth of the artificial intelligence industry, people have shown concern for responsible use, privacy, and data safety. We share that concern. For HANCE safety is not just a nice-to-have, but a competitive edge.

HANCE does realtime AI audio enhancement. In other words, we create different algorithms using machine learning, and then sell those algorithms to companies that need some form of realtime AI audio enhancement.

At the end of the process our algorithm is locally stored on our customers’ products, whether it is a microphone in a headset or video conferencing software. Our AI runs locally. No internet, no cloud = no remote hacking, by design.

How it’s made

To train a machine learning algorithm we need data. All our data and training material is legally acquired. The fact that this is an actual selling point shows why there is reason for concern over the AI industry.

Our AI’s are developed and trained by our own developers in Norway, no outsourcing. In addition to that we only use secure EU servers for storing data used to train our machine learning algorithms.

Our customer gets a model  that they integrate into their software or hardware. At this point we have no control over the copy our customer has received.

Who needs this kind of safety?

The audio industry includes anyone who uses digital sound, software and hardware. From hearing aids and baby calls, to smartphones and supercomputers: 

The music industry, video streaming, consumer electronics, emergency services, military communications, surveillance, telecom, aerospace, contractors, and the medical industry - the list goes on and on.

All these industries have different safety and privacy needs – but the way our algorithms are created and used ensure we meet the requirements of privacy, safety, and responsibility in all of these use cases. 

• Legal training data
• Local development
• Locally run on devices
• Local integration for our customers

This is why HANCE has a competitive edge.