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Minsky® AI Engine

Enterprise Level, Most Powerful, and Easy-to-Use AI Engine

 

The Challenge:

 

With the current Global requirements to use Artificial Intelligence for various applications and across various verticals it is extremely complicated and time consuming to develop an AI Solutions to make any predictions from a given dataset. This is because of the various possibilities that need to be considered such as programming knowledge, selecting the right Model/Algorithm (s), selecting the correct data variables, selecting the correct historical data, performing the initial tests and finally for optimizing the models in order to achieve a high prediction accuracy.

 

The Solution:

 

Our Minsky AI Engine provides an easy 4 step process that allows you to easily test and model your data irrespective of the industry type or data size and go live very quickly.

Using of AI Engine, we provide outcome based turnkey solutions, blending industry best practices & ideas to fulfill customers’ expectations.  We help our clients to achieve organizational objectives & maximize their Return on Investment (ROI).

Key Benefits of Minsky:

  • User-Friendly, cloud-based AI platform

  • No coding skills are required for results or predictions.

  • Provides you a list of % dependency features that can be used to optimize your business

  • Ability to fine-tune or optimize the models by trying different algorithms/prediction attributes.

  • Easy integration with other third-party solutions such as TABLEAU for data visualization.

What Sets Minsky® Apart?

Minsky for Out of the Box Deployment:

Our AI engine Minsky is named after the great pioneer American Cognitive and computer scientist Marvin Minsky who defined AI as “the science of making machines to do things that would require intelligence done by human beings”.  Just in 4 easy steps, you can easily model your historical data and produce results with real time data irrespective of the industry type and data size. Typical solutions can go live in less than a week!

Data Gathering:

In order to make any AI/ML/DL predictions, we need to start with the historical data that your business generates. In most cases the data might come from several disparate sources. We simply ask you to relay that data to us and our engineers will sanitize this data in an established systematic manner that enables the customer to find a solution to the dilemma that needs to be solved.

 

Training:

Here we take the good values for all the weights and the bias from labeled examples. We then use the supervised/unsupervised learning techniques, where the selected algorithm(s) build a model by examining many examples and attempts to find a model that best fits your data for the required prediction.

 

Model Building:

An artificial neural network model is based on the calculation of linear formulas and activation functions with weights, biases (i.e., their “settings”) being adjusted with each calculation. This Model is built based on your historical data and used for future predictions.

 

Prediction:

This is the output of an algorithm after it has been trained on a representative dataset (historical data) and applied to the new data when forecasting the likelihood of a particular outcome.