“MY” Technologies


ATRIVU’s initial design of an Immersive Learning platform couples the vision of asymmetric non-linear learning (CONTEXT) via our synthetic MHP (Model of Human Performance) with our “Evolving Cognitive Animation” component, has been extended to present solutions for Customer Service as well as a Virtual Pharmacy Representative, with the 3 solutions referred to as MY Technologies or “MYT”

Why Context before Content? 

We know that “content” has a completely different meaning if it is put in a different “context”.  In order to determine the best information to provide, we first need to identify what is behind the request.  All learning has “context” as well as historical relevance.  This context and relevance is based not so much on the content of the information that was used to learn, but the meaning and emotion we associate with it.  By understanding this we are given the ability to extend our learning events beyond simply looking for information, and guessing whether or not we have asked the right questions.

Within the learning environment In order to create the learning engagement that will capture both the attention of the user and facilitate the transfer of information to continue the knowledge transfer cycle (KTC), we must create an environment that allows for a seamless knowledge transaction.  KTC is just like the communication cycle, it presents information, that information is received, and feedback is provided back showing either a positive or negative outcome.

MYT™ can be integrated into all existing systems of the Enterprise.  We are packaging the technology as Software-as-a-Service and adding powerful interfaces and APIs (Application Programing Interfaces) to eliminate Client frustrations with the complex aggregation and interpretation of data from their disparate technologies, including business intelligence systems and network topologies.

At its core ASIS™ proactively incorporates shared data from current and legacy system applications to analyze, evaluate, process, fuse and interpret in real time.  Automated responses based on pre-determined rule sets and machine-learned protocols proactively manage critical systems in real-time.  The byproduct is enhanced enterprise functionality and offset of technology expansion and transition cost.