EndurGen is a software module that generates labeled patterns for any data input – structured, semi-structured, or unstructured. It is highly flexible to add into your analytics data pipeline both on desktop GUI applications as well as server-side large-scale data processing engines. The cross-platform and technology agnostic design of the products enables users to add it to the existing pipelines in no time and get the results fast.

One of the biggest challenges in implementing machine-learning technology is the need for quality input data. Today, there is a huge amount of data produced by users and systems but these raw data need to be filtered and labeled to be used for training AI algorithms to solve specific problems such as identifying client data change for KYC (Know Your Customer) and spot anomalies or fraudulent activities.

As explained in the picture here, the difference between traditional programming and machine-learning is the need for quality “labeled data” that needs to be provided as “Answers” so that the AI can produce tuned algorithm models as opposed to hand-written code rules in the traditional programming. These trained models are used to predict new incoming data. The accuracy of the prediction of the machine-learning algorithm depends on the quality of the “labeled” data or “Answers” used to train the system.

EndurGen software in an execution module that can extract “pattern” from any raw data passed through. This generated “pattern” can be further used to “label” the input data much easier, faster and can be used to train AI algorithms to produce “rules/models” to solve problems at hand. EndurGen software can be easily introduced into any data pipeline for machine learning and data science. It can also be customized to specific domain problem space. For example, the analytics data processing pipeline created using analytics platforms such as Alteryx, Ab Initio, Informatica, and Pentaho. EndurGen software can also be plugged into data science and reporting platforms such as Tableau, PowerBI, Spotfire, and Cognos.

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