Acta Scientific Computer Sciences

Review Article Volume 4 Issue 7

A Call-based Natural Language Classification API Framework for Conformity Assessment of Artificial Intelligence Application in an Organization

Giuliana Gois1, Nishant Parthasarathy1, Ravneet Kaur Ajji1, Shalbin Benny1 and Parisa Naraei2*

1Member of the Working Group, Canada
2PhD Research Investigator, Lambton College and Cestar College, Canada

*Corresponding Author: Parisa Naraei, PhD Research Investigator, Lambton College and Cestar College, Canada.

Received: June 02, 2022; Published: June 23, 2022


This proposal aims to solve for the issue of customer care executives not following the compliance metrics set by the company by using Artificial intelligence to create an application that allows the companies to monitor the calls using the compliance monitor and taking the necessary steps to give better customer service.

The proposal suggests transcribing the audio files that are to be checked for compliance issues, and using Google Cloud Platform (GCP) [1] for this purpose. upon completion of this phase, the compliance monitor model will process the transcribed data and in the final phase, a manual cross referencing to be performed to ensure the absence of the compliance issue in the audio files.

The final output by the AI marks all the compliance issues along the path of the set compliance rules that was set by the organisation. Each compliance issue will be highlighted in the transcript along with a color code that will describe the compliance issue that was not followed.

Using this compliance monitor, we can assure that all calls are met with the certain standards set by the organisation and instead of checking random calls out of a pool, all calls can be monitored flawlessly

Keywords: Artificial Intelligence; Google Cloud Platform (GCP);


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Citation: Parisa Naraei., et al. “A Call-based Natural Language Classification API Framework for Conformity Assessment of Artificial Intelligence Application in an Organization". Acta Scientific Computer Sciences 4.7 (2022): 55-60.


Copyright: © 2022 Parisa Naraei., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Acceptance rate35%
Acceptance to publication20-30 days

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