Stream Analyze has quickly gained momentum on the automotive market across Europe, engaging so far with manufacturers of cars, trucks and forklifts. The automotive industry is collecting more data than even their wildest projections suggested, the amount of data expected to be captured is many multitudes higher than what was calculated. Pushing all that data up into a cloud is just not feasible, needing to do the analysis on the car directly. Tesla is a cornerstone example of how the industry is transforming. Tesla has driven the automotive industry into a huge change, because what Tesla’s saying is a car should be electrical, it should be powerful – it should be software on wheels. This is disrupting everybody, because they’re now forced to move away from mechanical engineering to software engineering. Tesla had set a global standard for continuous product development – and now by utilizing Stream Analyze edge AI technology, companies can do it as well, or better, than them. Quickly.
Our product, sa.engine, is a software platform with three strengths:
1) a capability to interactively search and analyze large data streams in real time, directly on a device without relying on the cloud,
2) a highly optimized architecture making it independent of operating systems and other software, also resulting in an extremely small footprint, and
3) ease-of-use allowing for interactive changes, queries, and development of models on the fly, without the need for programming skills.
AI analysis of real-time data has never been hotter than it is now and is currently the most disruptive on the market in virtually any industry. Furthermore, almost endless possibilities are created by connecting sensors on virtually all machines and appliances globally. By being able to quickly analyze streaming data generated by sensors in real time, we can make better decisions, automate processes and understand how our machines and appliances work. A highly limiting factor at present is that all data is sent to the cloud for analysis, storage, and decision. This is often impractical and unsustainable for several reasons: firstly, large amounts of data often need to be quickly analyzed in real time. Secondly, the devices are not always connected to the cloud. Third, it is often not desirable for business-critical knowledge and individual information in data and models to be sent to the cloud. Therefore, it is interesting to be able to analyze data where it is generated.
