Spinnakr is part of the Orange Fab-San Francisco Season 5. Michael Mayernick explains what Spinnakr’s business is all about.

What does your solution involve?  
MM : We offer a streaming analytics product. It is really a single service.

What makes your product unique?
MM : We implement Artificial Intelligence in our service, in terms of machine-learning algorithms. One thing that makes our product very powerful is that when users and businesses enter their data, our service automatically builds statistical and machine-learning models. It does this for each data stream, individually.
The streams are processed in real-time using algorithms. The machine-learning technology also includes predictive technology. We see trends and extrapolate. For instance, we can monitor changes in skills in the market for job searches.

What kind of streams can you analyze?
MM : We deal with all kinds of streams: web marketing data generated by visitors to a website, social media, tweets, and digital-add data, etc.
We also work with sensor data generated by the IoT, smart cities, infrastructure monitoring, educational and chart data, etc.

What is the “wow factor” offered by Spinnakr?  
MM : The “wow factor” is in the combination of real-time processing, and in applying the algorithm in real time as well. What is significant about this is the data that they gather. They can respond to anything that happens immediately. We can provide assistance to businesses with this tool.


How is the “learning” implemented in the system?
MM : Our learning takes place separately in each stream. Why? Because each stream has its own characteristics, which means that the machine-learning process is learning from each separate stream.
It is then able to build a model. Instead of each stream requiring teams of data scientists to study it, the software learns automatically. It therefore requires training time. We have to look at a stream for a while. We can’t start on day one. The algorithm needs to work.
Who is your audience?
MM : Anyone working with data…


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