Case Study: How Superteams.ai Built Sentiment Analysis Engine for a Telecom Company

Client Overview

‍The client is a leading telecom company headquartered in Singapore with a branch in Australia, managing customers across various verticals and handling over 100,000 customer service calls daily through their call center solution for e-commerce clients. They sought to introduce a new feature that would enable them to extract meaningful insights from call recordings and gain actionable data to enhance customer interactions.

The Challenge

 The client’s internal systems recorded over 100,000 calls daily for their e-commerce clients and securely stored them in their internal Amazon S3 bucket. However, extracting actionable insights from this immense volume of data presented significant challenges:

1. Data Processing at Scale: Managing the ingestion and analysis of such a high volume of audio recordings daily.
2. Sentiment Analysis: Building a reliable system to analyze customer sentiments from call recordings.
3. Custom API Development: Developing an API to query and integrate AI-analyzed insights into their existing SaaS platform.
4. Knowledge Transfer: Enabling the client’s internal team to manage and maintain the system independently after deployment.

Attempting to achieve this internally would have required hiring and training full-time AI and software development resources, which could have led to significant costs, delays, and uncertainty about the project’s success.

The Solution‍

Superteams.ai leveraged its expertise in rapidly building AI-powered systems to deliver a comprehensive solution tailored to the client’s needs. Over four months, we assembled a team of vetted AI developers and engineers to design and implement a scalable sentiment analysis AI system using the following approach:

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1. Speech-to-Text Conversion:
   • Leveraging OpenAI’s Whisper model, we implemented a robust pipeline to transcribe audio recordings into text with high accuracy. The system effectively handled language nuances and accents common in Australia.
2. Call Content Analysis:
   • Using the advanced Llama 3.1 Large Language Model (LLM), combined with the Outlines library, we summarized each call’s contents into structured outputs. This included identifying key topics, customer concerns, and agent responses.
3. Sentiment Analysis:
   • The system used an LLM to understand customer sentiment on each call and provide detailed insights into the conversation and pain points as structured output.
4. Data Streaming and Processing:
   • Apache Kafka was integrated into the stack to ensure seamless real-time streaming and processing of call data, enabling the system to handle the ingestion of large volumes of audio recordings efficiently.
5. Data Storage and Retrieval:
   • Summaries and sentiment analyses were stored in an open-source vector database. This allowed the system to perform similarity searches with pre-filtering (based on customer details, agent details, time of call, and other metadata). The storage cluster was configured with replication and sharding for high availability and scalability.
6. Solution Architecture:
   • The architecture featured a modular design, including:
     • Data Ingestion Module: Handling call data streaming via Apache Kafka and preprocessing audio files. Key components were containerized using Docker to ensure portability and consistent deployment across environments.
     • Transcription Module: Implementing Whisper for transcription and Llama 3.1 for advanced natural language understanding. Kubernetes was used to orchestrate containers, ensuring high availability and scalability of the processing services.
     • Vector Search Module: Utilizing Amazon RDS for PostgreSQL with PGVector for indexing vector embeddings and metadata, similarity search and rapid retrieval. 
     • API Gateway: Built using FastAPI to manage secure and scalable access to the processed data. The gateway was deployed in a containerized environment, ensuring easy scaling and updates.
     • Visualization Layer: A Next.js-powered assistant UI for querying insights and presenting dashboards, containerized to allow seamless deployment and integration with the backend.
7. API and Assistant UI:
   • We developed a secure FastAPI endpoint, enabling the client to integrate call analytics seamlessly into their internal systems. Additionally, a simple assistant UI using Next.js was created for non-technical team members to query and visualize data.
8. Knowledge Transfer:
   • Superteams.ai worked closely with the client’s internal team throughout the project, training them on the architecture and code. This ensured they were well-prepared to manage and expand the system post-deployment.

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The Impact

• Expert Team Assembly: Superteams.ai quickly assembled a team of vetted AI developers and engineers, ensuring the right expertise was in place to deliver results efficiently.
• Faster Insights: The client's older system was slow and prone to inaccuracies, making it difficult to extract meaningful insights from call data. The AI-powered call analysis system developed by Superteams.ai significantly improved performance, providing structured and accurate sentiment data rapidly.
• Enhanced Customer Understanding: With detailed sentiment analysis, the client gained actionable insights into customer satisfaction trends and recurring issues.
• Cost Savings: The project was completed in four months without the need for the client to hire, train, and manage full-time development teams.
• Competitive Advantage: By quickly jumpstarting their AI capabilities, the client stayed ahead of competitors in leveraging AI for customer experience improvements.
• Scalable Infrastructure: The solution was designed to grow with the client’s data needs and seamlessly integrated into their existing workflow.

Conclusion

‍Superteams.ai’s flexible approach to assembling on-demand AI development teams enabled the telecom company to achieve its goals efficiently and cost-effectively. The project highlighted the impact of using on-demand AI teams to rapidly build advanced features, avoiding the costly process of training internal teams or hiring new developers.

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