Custom AI call center software for Telecom
AI-powered Telecom call center processes automation and optimization
40%
reduced wait times
25%
spike in client satisfaction rates
34%
lower client churn rates
Challenge
Optimize customer interaction quality, reduce client churn rates, and integrate call center operations in a single robust AI-powered software system.
Solution
Development of a custom comprehensive AI-based software solution for call center managers.
Client
Our client is a renowned provider of internet, television, and mobile communication services in Eastern Europe. The software, the company’s call center had been relying on for 7+ years, didn’t meet the evolving needs and requirements of the the telecom business and was unable to optimize customer processing efficiency, which led to unsatisfying client loyalty rates. To stop the outdated legacy software from dragging our partner’s business indicators further down, Modsen AI team got down to work to build a cutting-edge AI-powered call center software solution.
Challenge
Given the desired functionality scope, legacy software modernization our client was initially interested in, would have been much less efficient in terms of time, resources, and final software capacity. During the first consultation with Modsen CTO, our partner decided in favour of starting from a clean slate and requested us to build a custom AI-integrated call center system for their business. The list of requirements encompassed the following tasks:
Utilization of ML algorithms to analyze and direct calls to the most appropriate departments.
Development of tools to monitor and analyze call data in real time.
Integration of AI tools to analyze customer sentiments based on their voice.
Development of analytical tools to provide insights into call volume, common issues, and customer satisfaction levels.
Integration of AI-driven call center chatbots to handle common customer inquiries.
Integration with CRM systems to provide call agents with comprehensive customer profiles.
Creation of automated workflows to resolve common issues without human intervention.
Automatic creation of customer request cards during the call.
Strengthen security measures to protect customer data.
Make the AI call center available 24/7 to assist clients at any time.
Development of detailed reporting features to track performance metrics, and custom reports to meet specific business needs.
Team
2
AI&ML engineers
3
Back-end developers
2
Front-end developers
1
DevOps
2
UI/UX designers
2
QA testers
1
Business analyst
1
Team lead
1
Project manager
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Development process
Requirements gathering
At Modsen, we know the value of minute details and the importance of being on the same page with the client from day 1. That’s why we make sure to kick off every partnership, new or ongoing, with a set of online meetups during which our project managers carefully document the client’s expectations and product requirements, identify project priorities, and set deadlines. Having conducted 3 online meetups with our telecom partner, our team drafted a comprehensive requirements document to rely on during the whole AI call center software development process.
Planning
During the Agile-based planning phase, our team conducted thorough project research and collaborated closely with the client to ensure all requirements were reflected accurately in the final project roadmap. Key activities included:
Setting clear objectives and timelines for AI-based call center project milestones.
Allocating resources and defining roles and responsibilities.
Identifying key performance indicators.
Developing a comprehensive project plan with risk assessment and mitigation strategies.
Ensuring compliance with industry standards and regulations for data security and privacy.
The preparatory stage was finalized by submitting the project plan for the development of the automated AI-powered call center software and receiving the client’s approval.
Team assembly
At Modsen, we place great emphasis on the project team composition and that’s why entrust it to our CTO. His knowledge of the unique strengths and competencies each engineer on the team possesses allows for 100% accuracy when it comes to finding a perfect match for the project’s needs and requirements. To assemble a team for AI call center software development, Modsen CTO handpicked the following experts:
1 project manager and 1 team lead: To oversee the project and ensure timely delivery.
2 AI&ML specialists: For developing the machine learning algorithms and AI components.
5 software developers: To build the AI call center solution and integrate it with existing systems.
2 UI/UX designers: To design an intuitive and user-friendly interface.
2 QA testers: To conduct rigorous testing and ensure the system’s reliability.
1 business analyst: To align the solution with business goals and customer needs.
1 DevOps engineer: To streamline the deployment process, manage infrastructure, and ensure continuous integration and delivery.
Design
Complex corporate systems often lack intuitiveness, making it complicated for personnel to use the software effectively, nullifying the very purpose the application was built. To ensure that the AI call center service platform will reach its business target, our Modsen Design Studio team worked closely on the following elements:
Responsive and intuitive user interface: Designing a dashboard for call center agents and supervisors with easy access to key features.
Convenient chatbot interface: Creating a conversational and intuitive interface for the AI chatbot.
Visual reporting tools: Developing comprehensive analytics dashboards for performance monitoring and reporting.
Project building
To develop a robust AI solution for call center automation, our team followed Agile strategies, allowing for iteration on the platform's features and functionalities, and implementation of continuous improvements based on real-time feedback. Each sprint cycle included planning, development, testing, and review phases, ensuring that the software met the desired quality standards and functionality goals.
Our team of experienced Kotlin software engineers, each with 7+ years in mobile development, designed a custom mobile app solution to streamline fleet management. By integrating real-time GPS tracking, smart dispatching, and predictive maintenance analytics, we built a high-performance, scalable application that reduced delivery delays and operational costs.
AI algorithms: Developing and training machine learning models for call routing, chatbot interactions, and sentiment analysis.
Integration: Ensuring seamless integration with the client’s existing CRM and telephony systems.
Backend development: Building a robust backend to handle large volumes of data and ensure system reliability.
Frontend development: Creating a user-friendly interface for both agents and customers.
Comprehensive testing
Apart from iterative testing that took place during each sprint, Modsen QA experts conducted rigorous testing before the system’s integration to ensure the top-notch quality of the software we’ve built.
The range of tests for AI-based call center system included:
Functional testing: To verify the AI’s ability to handle customer inquiries, route calls correctly, and provide accurate responses.
Integration testing: To ensure that the AI-based system integrates seamlessly with existing call center infrastructure, databases, CRM systems, etc.
Performance testing: To assess the software’s responsiveness, speed, and stability under various load conditions to ensure it can handle high volumes of calls and interactions without degradation in performance.
Security testing: To evaluate the system’s defenses against potential security threats, ensuring data protection, user authentication, and compliance with data privacy regulations.
Usability testing: To test the UI/UX to ensure the software is intuitive and easy for AI call center agents to use.
Stress testing: To determine the software’s robustness and stability by pushing it beyond normal operational capacity to identify potential breakpoints.
Regression testing: To ensure that new updates or changes to the software do not introduce new bugs or negatively affect existing functionalities.
Voice recognition testing: To test the AI’s ability to accurately recognize and process spoken language, including various accents and dialects.
Data validation testing: To ensure that the data processed and generated by the AI is accurate and reliable.
Integration
During this phase, our team ensured smooth integration of the AI call center software with the existing corporate CRM and telephony infrastructure and conducted training sessions for call center agents to help them navigate the software and use its capacities to the maximum extent.
Servicing
Even impeccably built software requires further servicing. To support our partner after the product launch, our team kept in touch to provide continuous system monitoring for any issues and implement regular updates of the AI algorithms and software to improve its performance.
Result
The implementation of the custom AI-based call center software resulted in significant improvements for our telecom partner. Namely, 4 months after the launch the client noticed the following changes:
Reduced wait times: AI-driven call routing and chatbots reduced average wait times by 40%.
Enhanced customer satisfaction: Real-time sentiment analysis and improved response times increased customer satisfaction scores by 25%.
Operational efficiency boost: Automated handling of common inquiries allowed agents to focus on more complex issues, improving overall efficiency.
Accurate data-driven insights: Detailed analytics and reporting enabled continuous improvement and better decision-making.
The AI-powered call center system transformed the client’s call center operations, leading to improved customer experience, increased efficiency, and a competitive edge in the regional telecom industry.
40%
reduced customer wait times25%
increased customer satisfaction rates34%
reduced customer churn rates