Based just north of Cincinnati in Liberty Township, OH., Psibernetix was created to fulfill intelligent system needs in both the public and private sectors.  The methods developed by Psibernetix founder Dr. Nick Ernest are ready to be applied to an enormous array of topic areas.  Unlike other approaches, a rack of computers and a team of full-time research scientists are not required in order to obtain intelligent autonomy for your systems.  This enables our business model of applying these techniques to many different projects and businesses with near-limitless growth potential.

While currently a small team, we have a strong network of experienced professionals in a variety of fields and partnerships in place with other members of Industry as well as Academia. We are ready to expand should your particular application call for it.  Psibernetix Inc. is ready to begin working with both local and international clients through a negotiable combination of on-site visits and tele-coordinating from our office on both short and long term contracts.

Our Founder, Dr. Nick Ernest

Nick is an Ohio native who spent nearly a decade at the University of Cincinnati for his BS, MS, and PhD in aerospace engineering and has worked for or with the United States Air Force for over 8 years.  In June, 2013, he began work on his first Genetic Fuzzy Tree, the Learning Enhanced Tactical Handling Algorithm, which was an incredibly successful application to a problem of extreme scale and complexity.  Nick’s passion for autonomy, machine learning, efficiency, and programming drove him to open Psibernetix and continue his work in this area.  Though his degrees are in aerospace engineering, Nick’s interests are in any topic area that can benefit from his methods.


  • 2015 – PhD in  Aerospace Engineering, University of Cincinnati
  • 2012 – MS in Aerospace Engineering, University of Cincinnati
  • 2011 – BS in Aerospace Engineering, University of Cincinnati

Publications Available via ResearchGate

Our Mother AI, EVE

In most AI / machine learning methodologies, some type of search algorithm is used to “teach” the system.  Very frequently, a Genetic Algorithm is employed, which is what the “Genetic” in “Genetic Fuzzy Tree” alludes to.  This algorithm is basically the software implementation of evolution and survival of the fittest.  “Populations” of potential systems are made, evaluated, and then bred to create new populations, until an answer is found that meets the desired criteria.

Our patent-pending EVE system takes the place of traditional Genetic Algorithms, but rather than be a passive search algorithm, EVE is herself a Genetic Fuzzy Tree.  The fact that EVE is a Genetic Fuzzy Tree that creates and optimizes other Genetic Fuzzy Trees means that she can teach herself how to best teach other AIs.  In every application thus far, EVE provides better performance, both in terms of results obtained and the time necessary to obtain said results.

Intelligent searching is but one of EVE’s many features.  For example, she can also intelligently utilize a network of computers; EVE autonomously searches for cores she has access to and even tracks their performance so she knows how to optimally use every resource available to her.

EVE is one of our primary tools for cutting both cost and development time, and is constantly improving to provide even more value to our clients.