Our robust library of algorithms have been successfully built over 50 years of R&D, case studies and real-life applications. At the core of our architecture we have a strong vision and use of Ai for supply and demand chain processes enabling speed, visibility and scalability for most industries.

This holistic ecosystem enhances decision support capabilities and high performance through advanced data analytics, machine learning and a keen ability to implement intelligent algorithms. A seamless network of mathematical Ai(s) that anticipate, act and adjust on-the-fly to anomalies, independent to human intervention.

As our global population expands exponentially supply chains are severely disproportional to demand chains resulting in missed opportunities, increased asset loss, and economic disparity. Lack of visibility to understand and measure workflow inefficiencies hinders the ability to service and fulfill operational demands.

These inherent old issues coupled with covid-19 have disrupted and paved new ways on how we operate and service our marketplace. This road is characterized by a hyper accelerated exchange driven by on-demand customer centric products, large scale logistics and last mile white glove experiences.

The brick-and-mortar set-up with four walls have tumbled down. Small to large enterprises need real-time visibility and operational optimization throughout their entire business chain. Seamless coordination with the robust and dynamic ability to predict and mitigate risks coetaneous across business units goes beyond automating equipment, human capital, and software integration.

The effective systematization and digitization of an enterprise to deploy and coordinate fully realized cyber physical systems (cameras, sensors, robotics, wearables, drones) that optimize the entire organization has superseded the contemporary use of data analytics and the software that communicate with these systems.

Advanced Analytics is a comprehensive set of mathematical predictive techniques and methods (such as statistics, data mining, simulation) and mathematical prescriptive methodologies (optimization). This enlightened solution is designed to help businesses discover trends and patterns, and accurately anticipate/predict the future to make the best decisions.

Today most applications skim the surface of real-time prediction and further more the ability of agents to self learn and respond autonomously in real time. These skills are integral to scale and ensure sustainability.

Built within our solution are the next steps for Advanced Analytics – Sparse Analytics. These frameworks are constructed around cognitive learning, similar to sparse distribution. Comprised of mathematical engines that learn from the prescriptive methodologies and technologies in order to deploy highly automated and autonomous optimization solutions that grow evermore intelligent in real-time environments.

MATH 4.0 is a concept linked to INDUSTRY 4.0. As a result it is the mathematics required for intercommunication with parallel multiple robots [mathematical Ai]. These concepts are from 1960 but have not been fully socialized. They remain at the level of large-scale optimization methodologies used to solve specific problems.

This is the beginning of an ubiquitous new vision for highly complex parallel mathematics in business management.

Many say and assume “by developing more software for machine learning” we’ll achieve today’s demands. Contemporary mathematical programming and machine learning has successfully allowed us to talk and control Ai. But these methodology and coding languages are not sufficient. They don’t keep up with current market demands governed by real-time customer centered products and the adequate visibility of knowing when those demands need to be served.

Even if we had an abundance of resources and human capital to write code we run into many organizational problems. How do we coordinate such a large group of engineers? How do we disenfranchise their heavily influenced cultural styles and methods to a more uniformed and seamless way of building code for the future?

At HYPOTHALAMUS Ai we believe these questions can only be addressed through a technological paradigm comprised of constraint based data analytics and complex mathematical modeling. This solution entails the composition of large scale and robust parallel algorithms aligned and distributed in real-time. To meet our current and future demands we need to design and implement machines that communicate independently among themselves. Autonomous systems that self-learn on the fly independent to human interference or need to write new code.

Many say and assume “by developing more software and machine learning” we’ll achieve today’s demands. Contemporary mathematical programming and machine learning has successfully allowed us to talk and control Ai. But these methodology and coding languages are not sufficient. They don’t keep up with current market demands governed by real-time customer centered products and the adequate visibility of knowing when those demands need to be served.

Even if we had an abundance of resources and human capital to write code we run into many organizational problems. How do we coordinate such a large group of engineers? How do we disenfranchise their heavily influenced cultural styles and methods to a more uniformed and seamless way of building code for the future?