As the automotive industry is shifting towards autonomous cars, higher levels of automation are embedded in modern cars, with AI/ML code taking a major role in self-driving car functions while collecting data from many sensors. While the objectives of car automation are to improve safety, reduce issues and fixes, enhance customer experience, and optimize car operation, there are multiple risks when things go wrong.  Yokaranda minimizes these risks by correlating millions of time series, identifying and alerting of abnormal application behaviour before it impacts the driving experience and passenger’s safety. 

Enterprise Software

Application Performance Management (APM) solutions have become, during the past two decades, a mandatory component in enterprise applications, ensuring the imperatives of application performance. While legacy applications rely on fixed algorithms and performance baselines, AI/ML applications behaviour and base lines are dynamic and complex. The challenge is not only to define dynamic baselines, but to identify if the change in application behaviour is a result of a change in application usage or a flaw in one of its components that requires a fix. Yokaranda is revolutionizing the AI/ML market with a modern and holistic APM solution that meets the dynamic nature and behaviour of AI/ML applications. By correlating deviations in application behaviour across thousands of instances, Yokaranda proactively alerts on performance issues and identifies the root cause of the problem, before it impacts the business.



The global IoT device management market is expanding at an unprecedented pace along with the growing demand for IoT services and the increasing penetration of communication technologies. AI/ML applications help manage and gain insights and intelligence from the overwhelming amount of data obtained from the IoT devices. Yokaranda monitors the AI/ML applications and will alert on deviation from normal behaviour that may lead to incorrect decisions and baselines, before customers are impacted.


The Manufacturing industry benefits from the Yokaranda solution in optimizing AI/ML based Asset Operation and Maintenance applications. By correlating millions of IIoT time series across multiple customer manufacturing plants, Yokranda is best positioned to identify and alert on deviations from normal manufacturing operations and help drive operation efficiency improvements and cost reductions.


Mobile Devices

Identifying deviation from normal behaviour, using the Yokaranda solution, will help smart watch and mobile phone vendors provide superior customer experience. By correlating millions of time series across hundreds of thousands of consumers, Yokaranda can identify abnormal AI/ML application behaviour and provide guidance on the root cause of the problem.

Smart Consumer Electronics

AI/ML software brings great potential to smart devices as it helps optimize their operations and enhance customer experience. This is true if the current software libraries are flawless, the sensors are operating as they should, and the environmental conditions are as expected. However, if this is not the case, the AI/ML application may analyze the data in an incorrect manner and adjust its behavior and baselines according to biased data. Yokaranda can identify deviation from normal behavior by correlating millions of consumer devices and proactively alerting on issues before customer experience is impacted. 


Smart City

Municipalities leverage advancements in the AI/ML technology to improve public services and safety and reduce operation costs. However, AI/ML applications may lead to wrong decisions in case there are issues with the software, the communication infrastructure or the data collected from sensors such as streetlights, video surveillance cameras and pollution detectors.
By correlating millions of time series, Yokaranda can identify deviation from normal behavior and drill down to the root cause of the problem, before it impacts the welfare of the residents.


Telecom operators leverage AI/ML applications to manage assets and operation of millions of CPEs (Customer Premises Equipment) with the ultimate objective of enhancing customer service and reducing costs. Deviation from normal behaviour of the asset and maintenance application may cause unnecessary and expensive on-site support calls and long and incorrect service processes that would have a negative impact on customer experience. Yokaranda can avoid this with its holistic view and correlation of millions of time series that would proactively identify and alert on deviation from normal behaviour of the asset and maintenance applications.