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Dialog Axiata, part of the Axiata Group, is one of the largest and most innovative telecommunications companies in South-East Asia. With millions of subscribers and more than 3,500 employees in Sri Lanka alone, Dialog has consistently led the region in adopting digital transformation initiatives that improve customer experience and operational efficiency.
As customer interactions continued to grow exponentially, Dialog faced a new challenge: how to deliver faster, more proactive support to millions of users while optimizing call center operations. Traditional reactive customer care models could no longer keep pace with the data-driven expectations of modern users.
To solve this, Dialog partnered with Fcode Labs to build an AI-powered prediction platform that could analyze millions of call records, identify behavioral patterns, and predict why customers were calling, before they even picked up the phone.
Handling more than a million customer care calls annually, Dialog needed a solution that could process massive volumes of data in real time and deliver actionable insights with precision. The challenge lay not only in building a highly accurate prediction model but also in ensuring it could integrate seamlessly with existing enterprise systems used by call center teams.
The project’s success hinged on four priorities:
Our collaboration began with extensive data analysis to uncover trends and root causes within customer interactions. Working alongside Dialog’s internal data science team, we analyzed telecom datasets to identify recurring behaviors and issues, forming the foundation for a powerful predictive model.
Using TensorFlow, Keras, and Python, we developed and trained a machine learning system that could accurately predict the likely cause of incoming customer calls. The model was continuously refined through iterative learning, improving accuracy with each new dataset.
To ensure scalability, we deployed the solution on AWS Cloud, integrating DynamoDB for real-time data handling and performance optimization. The architecture was designed for continuous monitoring, auto-scaling, and security, capable of processing hundreds of thousands of customer interactions efficiently.
Finally, our engineers integrated the system directly into Dialog’s enterprise environment, making predictive insights instantly accessible to customer care teams. The deployment was seamless, requiring no disruption to live operations while empowering staff with data-backed intelligence that reduced response times and enhanced service quality.
Our collaboration began with extensive data analysis to uncover trends and root causes within customer interactions. Working alongside Dialog’s internal data science team, we analyzed telecom datasets to identify recurring behaviors and issues, forming the foundation for a powerful predictive model.
Using TensorFlow, Keras, and Python, we developed and trained a machine learning system that could accurately predict the likely cause of incoming customer calls. The model was continuously refined through iterative learning, improving accuracy with each new dataset.
To ensure scalability, we deployed the solution on AWS Cloud, integrating DynamoDB for real-time data handling and performance optimization. The architecture was designed for continuous monitoring, auto-scaling, and security, capable of processing hundreds of thousands of customer interactions efficiently.
Finally, our engineers integrated the system directly into Dialog’s enterprise environment, making predictive insights instantly accessible to customer care teams. The deployment was seamless, requiring no disruption to live operations while empowering staff with data-backed intelligence that reduced response times and enhanced service quality.
The result was a transformative leap in how Dialog approached customer service. With machine learning at its core, the platform enabled support teams to anticipate and resolve issues proactively, often before customers reached out.
By embedding intelligence into the heart of customer care, Dialog Axiata strengthened its position as a regional leader in digital innovation, proving that with the right data, every customer interaction can become smarter, faster, and more meaningful.
The result was a transformative leap in how Dialog approached customer service. With machine learning at its core, the platform enabled support teams to anticipate and resolve issues proactively, often before customers reached out.
By embedding intelligence into the heart of customer care, Dialog Axiata strengthened its position as a regional leader in digital innovation, proving that with the right data, every customer interaction can become smarter, faster, and more meaningful.