Sergey Shcherbakov, Head of Data and ML Group, ICL Services

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Sergey Shcherbakov, Head of Data and ML Group, ICL Services

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One such tool that can help businesses improve the quality and speed of customer service is the Gen.AI product , developed by ICL Services. Thanks to the ability to easily integrate into existing business processes and adapt to the needs of a specific enterprise, the product allows you to optimize the process of processing incoming requests, automate routine tasks related to analyzing and responding to requests, and significantly speed up the response to requests of various types - from simple questions to complex problems. Sergey Shcherbakov, Head of the Data and ML Group at ICL Services , tells us more about the work of Gen.AI.


IT Channel News: Please tell us about the product itself first.

Sergey Shcherbakov: Gen.AI is a product based on venezuela telegram database artificial intelligence that allows you to automate, speed up, and facilitate the process of processing incoming requests. Moreover, a request can mean almost anything, depending on the specifics of the customer's business task. These can be incidents from users that are created in the ITSM system, or requests from citizens who want to make an appointment at an institution or clarify a problem. These can also be letters to customer support, reviews, or messages in a corporate chat. Our product will easily process all these and many other types of requests.

IT Channel News: How did the idea for creating the product come about?

S. Shch.: In 2020, we at ICL Services released a product called the Automatic Smart Assistant — ASA. That same year, it was recognized as the best IT project of the year as part of the Time of Innovations award. ASA, using machine learning technologies, analyzed the texts of user requests or incidents, classified them according to the required level of expertise, and formed a knowledge base for faster resolution in the future. As a result, employees' working time was saved, and the time for accepting an incident for work was reduced by 82%.
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