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CASE STUDY

AI Chatbot for Telecom – Customer Support

INDUSTRY

Telecommunications

USE CASES

Conversational AI Customer Support Automation Real-Time Monitoring

BUSINESS IMPACT

Cost Reduction Efficiency Improvement

TECHNOLOGIES

AWS Azure CRM API integrations Generative AI Knowledge base systems API integrations LLM-based Systems Natural Language Processing (NLP) Python Real-time processing systems

Client Overview

Industry

Telecommunications

Region

Europe

Company Size

Enterprise
500+ employees

Profile

Large telecom operator serving a high volume of customers across mobile and digital channels

Project Background

The client handles a significant number of daily support requests across web, mobile, and messaging platforms and required a scalable way to improve response times and reduce operational load.

The challenge

Customer support operations relied heavily on human agents and fragmented systems.

This resulted in:

  • High volume of repetitive queries
  • Long response and resolution times
  • Inconsistent answers across channels
  • Limited ability to scale support without increasing headcount

The client needed a system that could handle high-frequency requests while maintaining accuracy and consistency.

What we built

We designed and deployed an AI-powered chatbot integrated into the client’s customer support ecosystem.

The solution included:

  • A conversational AI system capable of understanding user intent across multiple input formats
  • Integration with internal systems, including CRM and knowledge bases
  • Automated handling of high-frequency support queries
  • Escalation flows for complex or unresolved cases

The system was deployed across multiple channels, including web and messaging platforms, and designed to operate continuously.

Impact in practice

The implementation enabled a more data-driven and responsive production environment:

Reduced support workload

by handling a large share of repetitive customer queries automatically

Faster response times

through immediate, always-available support

More consistent customer experience

across channels through standardized responses

Improved scalability of support operations

without proportional increases in support staff

Technologies used

Several technologies were used in order to deliver this project for the client, among which:

CRM API integrations
Generative AI
Knowledge base systems API integrations
LLM-based Systems
Natural Language Processing (NLP)
Python
Real-time processing systems

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