simMachines started with a passion
We focus on telling our clients the WHY behind each prediction, enabling our algorithms to comply with the needs and expectations of marketing and fraud / compliance executives, as well as the most stringent Artificial Intelligence auditing rules. Our predictions are contextually relevant, highly actionable and transparent.
We work with a wide range of global channel partners and leading brands to solve complex problems, bringing innovative solutions to market, and creating analytic and campaign workflow efficiencies. simMachines provides the most comprehensive set of nearest neighbor-based clustering, discovery, prediction, and regression algorithms available in the world.
History of Commercial Deployments Leading to Launch
simMachines was contracted to create a technology solution that could help accelerate the process for evaluating ICANN’s gTLD (domain name) applications while at the same time bring consistency to the process. The solution replaced a manual, error prone and expensive process run by a big five consulting firm to evaluate each application and determine who should receive which domain name. This project got the attention of the Internet infrastructure community.
Further investigation by ICANN into the reliability of the Internet based on the introduction of new domain name gTLD’s led to a pattern detection solution that pointed to a Windows Active Directory security vulnerability. With 1.2 billion Windows PCs deployed globally and using the Active Directory, this was a major security vulnerability. Resolution of the issue was major and it gained significant notoriety, including from the Department of Homeland Security.
simMachines technology was applied to find similar trademark applications with the purpose of detecting counterfeit good websites or security threats such as “phishing” attacks where somebody will reserve a domain name such as façebook.com that visually resembles a real domain name. Multiple machine learning competitors attempted to accomplish this but could not approach the power of simMachines proprietary similarity based methods.
simMachines technology was applied by a drug discovery company because they wanted to solve similarity queries but could not find technology for doing it effectively. simMachines technology was chosen because it could solve this difficult problem in sparsely populated, high dimensional data.
Arnoldo’s technology was one of 11 out of 721 machine learning companies spanning 50+ countries that was evaluated by Microsoft as part of the accelerator program. simMachines demonstrated the power of similarity in providing “the Why” behind predictions particularly in the marketing and advertising world where the technology is capable of creating dynamic predictive segments that adjust themselves depending upon the required business objective.
simMachines, Inc.’s commercial launch of the software includes high scalability similarity based software that can be deployed in the cloud or on premise for marketing, fraud and compliance. Applications are widely recognized by large global brands and technology and consulting partners as the only machine learning method that can provide transparency at scale and speed behind every prediction.
ROBERT ZIESERL, CEO
Robert is a serial entrepreneur bringing over 30 years of experience growing technology companies and also as a venture capitalist. He has successfully run five companies including predictive analytics, imaging, device and real-time data acquisition and control systems.
DAVE JAKOPAC, CHIEF CUSTOMER OFFICER
Dave has worked more than 25 years in the areas of Artificial Intelligence, Data Analytics, 3D Computer Graphics, and Robotics. He has co-founded five companies all with successful exits or still operating. He has a Ph.D. in Electrical Engineering and Computer Science from Northwestern University.