Since entering the market, DecisionIQ has secured marquee customers from Coca-Cola to Southern Company. Our Genesis IIoT platform produces distinctly superior results. In fact, all our POCs have been recommended to deployment, with complete cost justification. Some examples of rave reviews from our customers...
"You've exceeded all our hopes!"
VP Operations, Fortune 100 Beverage Company
Two other analytics companies unable to provide a solution.
"You're the only analytics company able to predict this problem. Well done!"
VP Product Engineering, Fortune 100 Communications Company
Three other analytics companies unable to provide a solution.
"Excellent work. You detected all the hidden equipment failures."
VP Innovation, Top Five US Electrical Power Utility
A former fighter aircraft designer at the General Dynamics F-16 skunk works, and now a serial entrepreneur with over 25 years experience. Andrew is a proven product visionary who builds great teams at the intersection of fast-breaking technologies and markets. Past innovations include first application of delta wings to optimize transonic fighter aircraft maneuverability, first multi-processor desktop computer (also built under a clone license from Apple) and an early cloud platform for video publishing. He holds a Bachelors in Aerospace Engineering from Georgia Tech and an MBA from the University of Chicago.
Nagi Gebraeel, PhD
Co-Founder, Chief Data Scientist
A visionary and world leader in the science of analytics for equipment health and failure prevention. Nagi is a Professor at Georgia Tech's top ranked Industrial and Systems Engineering school (ISyE) school, and brings 15 years of focused analytics research on real-world projects with major industrial partners and the National Science Foundation. There are over 1,200 peer citations to his research. Nagi Director of the Center for Predictive Analytics and Real Time Optimization, and Associate Director for analytics at Georgia Tech's Strategic Energy Institute. He holds a Masters and Doctorate in Industrial & Systems Engineering from Purdue University. LinkedIn...
Principal Data Engineer
Christopher completed his undergraduate studies at the University of Michigan in computer science. He worked as a full stack developer for over six years, including time as CTO in a funded startup building a web based platform for online learning. He recently received his Masters of Science in Analytics from the Georgia Institute of Technology and is excited to bring his analytics skills back to the startup sector. LinkedIn...
Principal Data Scientist
Michael began writing and coding machine learning and optimization algorithms in high school, focusing on the stock market and sports analytics. He received a Bachelor's degree in Computer Science from Auburn University before going on to receive a Masters in Analytics from Georgia Tech. Michael worked as a graduate research assistant at Georgia Tech, focusing on anomaly detection algorithms and implementations for large distributed datasets. He has also worked as a software engineer and data scientist for several larger companies before finding his passion for start-ups with DecisionIQ. LinkedIn...
Join a top team of data scientists and engineers able to tackle enterprise class IIoT problems. Learn from the best from a technical team that has been building IIoT solutions since 2003. We are looking for the best. If you're one of the best then help lead us forward. If interested, contact us via the form below with a description of your interest and skill set.
Commercializing University Research
This research is based in the #1 rated Georgia Tech School of Industrial and Systems Engineering. Under the leadership of Dr. Nagi Gebraeel, the research has been underway for 15 years, 25 man-years of combined work. It is centered in four research areas, each bringing unique facets of real world application. It has been integrated into new platform, the Genesis IIoT.
Analytics & Prognostics Laboratory
A very unique aspect is the use of an equipment failure test lab, the Analytics and Prognostics Laboratory. It set the stage for using empirical methods to build a new generation of real world algorithms, developed with a systematic approach of measuring actual equipment failure and testing results against predictions. Funded by the National Science Foundation and major industrial partners ensures both real world and highly challenging problems and commensurate results.