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The Core Convergence Solution

Core Convergence uses a combined team approach that comprehensively addresses corporate needs and combines a full suite of services to create products that utilize security, data science, Machine Learning, and blockchain components. With this methodology we have been able to successfully improve enterprise systems and maximize that success by taking a structural view of the company across each of these verticals. 

Consulting at the Cutting Edge

Core Convergence addresses the needs of the enterprise, security, and blockchain ecosystems to create the tooling necessary to solve these problems at machine speed.

The World is Changing

Our traditional ways of handling business, commerce, and human communications have assumed the stability of this slow moving progression of data. However, recent innovations have driven us into a situation where data is expanding exponentially on a daily basis at machine speed.

It is no longer feasible for humans to completely comprehend the amount of data expansion that is occurring, let alone manipulate that data. This is causing an increasing gulf between human and machine powered decision-making. Handling data and decision-making passively is no longer possible—computers are powering the transformation from reactive decision-making to forward predictive modeling and analysis. This is also rapidly changing the definition of data silos and how data is organized, not to mention what is fundamentally possible with data.

As the quantity of innovation compounds and available data exponentially increases in volume, we are led to a situation in which the entire system is undergoing a qualitative shift. The old models of handling business problems will no longer be competitive—and whole industries will either be altered or disappear in very short periods of time. 

The Problem

We have reached the tipping point. Now properly researched and implemented, the perpetual expansion of data and machine-speed data analysis will impact every industry on the planet. Historically, most industries have survived intact with minimal changes from generation to generation despite technological innovation. Even in the data science and Machine Learning fields, progress has been relatively slow over the past 40 years.

This time is different—we have the techniques, machines, and power to impact almost all human behavior through the combination of devices, data, data science, and decision making optimized through Machine Learning. Because machines can learn various forms of supervised, unsupervised, and competitive learning techniques that humans aren’t particularly well suited to understanding, it is possible to accomplish objectives at vast scales and at high speeds. 

Data Science and Machine Learning

Data Science has a vast array of tools and techniques to work with this new, ever-expanding pool of data. Through data science experimentation and discovery we can ask and receive credible, scientifically based answers to critical business questions and scenarios.

Properly combined with Machine Learning, data analysis can now be done at machine speed instead of human speed. In this context, Machine Learning is where advanced algorithmic techniques are used to produce a system in which a machine can learn across a vast pool of data and information, using techniques that vastly accelerate what a human—or a team of humans—could previously do.

By combining data science and Machine Learning we can improve not only the analysis of data itself by addressing huge quantities of ever-expanding information, but can also address data in real-time versus post-facto analysis. When data can be put into models in real time, we can much more quickly and accurately predict the future.

Our Specialties:
The Security Industry and Blockchain

The importance of Machine Learning and Data Science is particularly evident in the security community, where a combination of factors are making its implementation inexorable. 

On the attacker-side, engineering effort has been put towards creating systems at scale that are machine powered and iterative to do bad things. By automating attack systems, collecting data at scale, and building offensive cyber-weapons to attack virtually every form of target on the globe, attackers are able to harness large-scale funding efforts to act at the nation-state level and collaborate in doing so. 

Most defensive systems are human powered at human speeds, and those systems are not even aware that they are being staged for an attack in real time. Security vendors lack product offerings that are data driven and Machine Learning driven. These industries are now dealing with scenarios in which attackers are using machine speed and collecting massive amounts of data to harm targets. Defenses have not kept pace, and don’t take into account the volume, quality, or speed of data that could be used to take countermeasures. 

Moreover, this issue is compounded by compartmentalization within the security space. Most security companies haven’t been immediately open to the idea of sharing large amounts of data across systems, vendors, and other companies to solve security problems in a holistic way. Attackers, however, are willing to share data and to cooperate to take down targets, thus giving them a structural advantage. Defenders are either unaware they should be sharing data, or if they are aware, but lack the structural and technical mechanisms to do so. They lack protocols and methodologies to share data in which they can trust the counter-parties in the data sharing exercise. 

This is, of course, a problem that many individuals in the blockchain industry feel that they are helping to solve—sharing data across disparate counterparties and aligning those counterparties in that data sharing effort. 


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