Optimizing Customer Support with Real-Time Call Sentiment Analysis Using AWS Bedrock

Company Name

Location

USA

Industry

Client

Glia is a B2B interaction platform specializing in facilitating communication between financial companies like banks, credit unions, and insurance companies and their customers.

Challenge

Glia generates millions of client call minutes monthly. Analyzing these calls in real time posed a significant business challenge. The company needed an optimal, technically efficient, cost-effective solution to improve customer support and care.

Solution

DataArt’s team deployed the optimized open-source Whisper model to Amazon Sagemaker inference endpoints for near real-time transcriptions. To conduct sentiment analysis, they used Claude-instant on AWS Bedrock, categorizing call transcripts segments as negative, neutral, or positive. The solution was integrated into Glia’s platform as a widget, providing operators with a real-time sentiment dashboard. The transcripts and sentiment labels are securely stored in the platform for further analysis.

AWS Lambda (SAM) was utilized to handle serverless compute tasks, ensuring efficient processing. Amazon S3 provided scalable storage for raw audio files and processed transcripts. Amazon EventBridge Scheduler was employed to automate and manage the workflow, ensuring timely processing of call data. Additionally, Amazon RDS for PostgreSQL was used for structured data storage and management.

The most time-consuming part of the process was selecting the optimal speech-to-text model for real-time call processing. Through extensive research and testing, the team found Whisper model performed best on English datasets for call transcription. Also, the fastest inference engine was chosen.

Technology

AWS Bedrock

Amazon RDS for PostgreSQL

Amazon DynamoDB

Amazon EventBridge Scheduler

Amazon S3

Amazon Lambda (SAM)

Outcomes

Achieved approx. 12-sec speech-to-text latency, and 13-16 sec sentiment analysis latency.
Transformed the solution into an applet and fully integrated it into Glia’s platform.
Equipped operators with access to all the available data on the platform and real-time call sentiment analysis, enabling more efficient customer support.

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Optimizing Customer Support with Real-Time Call Sentiment Analysis Using AWS Bedrock​