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KREDOR Prediction Framework!       

Prediction engines are often used to predict sales and other results of human acitivity, which depend on the complex interplay of many dynamic factors. They are trained on historic data using machine learning algorithms. The best systems don't require a lot training data, are good at modeling the dynamic nature of people's behaviour, and achieve superhuman accuracy.

For example, a company's sales may be related to factors such as the previous days sales, the day of the week, the day of the year, the exchange rate, the business growth rate, the time elapsed since the last peak in sales, etc. Some deployments of our framework account for more than a hundred different factors.

Main street businesses typically face cut-throat competition, where every cent matters, and require very high prediction accuracy to succeed. KREDOR Prediction framework is designed for these extreme requirements.

It integrates seamlessly with your software infrastructure due to its API and customizability.

With our help, getting your own auto-decisioning system up and running is a breeze.

Top 5 reasons To Use KREDOR Auto-Decisioning Systems

There are many compelling reasons to use KREDOR Auto-Decisioning Systems.  These are the Top Five:

Speed Up Your Software Development By At Least a Factor of Two Can accomodate even the most sophisticated needs.
Convenient API makes it very easy to integrate in any system.
Create Object Oriented Software that Effectively Models a Business High Performance
Has high throughput and exceptional accuracy.   
Reduce Code Complexity Convenient User Interface
A sophisticated user interface that includes automatic intellisense can be easily developed from the provided template and given to business experts to manage.
Secure Your System Proven Solution
A number of companies have already have build highly successful solutions with KREDOR Prediction Framework.
Ensure Data Integrity Save time and resources.
Get a great rules-engine solution, specific to your needs, at a fraction of the cost and time it would take trying to develop an in-house solutions (which may even turn out impossible).   

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