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What is the solution ?
Building a custom , automated architected solution effectively acts as a feature engineering pipeline that is able to capture semantic information in the interactions between call centre agents and customers .
This is possible due to recent breakthroughs in automatic speech recognition , and natural language understanding technology , as well as the direction in which the natural language processing , research community has been heading .
the call was made and received , the duration of the call , in which queue the client may have had to wait , and which recorded messages did the client listen to .
From Salesforce , we receive information on the type of interaction such as which product was discussed , what kind of service was provided , whether there were previous interactions relating to this sale , service request , and more . This data is aggregated in either S3 , a unique directory structure for each live stream that is recorded or in relational databases , metadata , explains Hugo .
Not only will this solution allow an organisation to be compliant , but it allows efficient scaling that could lead to processing 100 % of call volumes for a significantly lower cost than the manual alternative .
Solution architecture
Given that this is a completely automated solution , the entire pipeline is set off by a scheduled trigger using Amazon EventBridge , typically , at the end of the day to allow all business for that day to be concluded , and Lambda functions that orchestrate the integration between different components .
The secret ingredient that makes this entire solution possible and so effective is the type of AI that is used in the inference component
By utilising Amazon Connect and Salesforce , metadata is captured as interactions , calls occur . From Connect , we receive the actual recording and information on the call itself . Some of this information includes which banker was speaking to the client , from which number
The scheduled run starts off by loading and linking the recordings and metadata from the Connect and Salesforce ecosystems . Connect ’ s metadata resides in a Redshift Data Warehouse and Salesforce has a proprietary data structure built into it that can be queried using their Salesforce Object Query Language , SOQL .
These artifacts are then transferred to the raw data repository in S3 where it is being viewed by S3 triggers that set of the succeeding components . Once new content is detected in the recording ’ s repository , a Simple Queue Service , SQS is created to push transcription jobs to Amazon Transcribe for the audio to be converted to text .
Amazon Transcribe is also capable of Speaker Diarization , allowing us to separate the client ’ s verbiage
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