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CASE STUDY

Internal AI Assistant for Telecom companies

INDUSTRY

Telecommunications

USE CASES

Copilots & Internal Tools Knowledge Retrieval Process Automation

BUSINESS IMPACT

Efficiency Improvement

TECHNOLOGIES

AWS Azure Generative AI Natural Language Processing (NLP) Python Retrieval-Augmented Generation (RAG) Vector databases

Client Overview

Industry

Telecommunications

Region

Europe

Company Size

Enterprise
500+ employees

Profile

Large telecom operator with multiple internal systems supporting operations, support, and technical teams

Project Background

The client manages a large volume of internal knowledge across systems such as ticketing platforms, documentation tools, and operational databases.

The challenge

Teams relied on multiple disconnected systems to access information, including documentation, tickets, and internal tools.

This resulted in:

  • Time spent searching across systems for relevant information
  • Inconsistent access to up-to-date knowledge
  • Repeated internal queries and duplicated effort
  • Limited ability to scale internal support efficiently

There was a need for a unified system that could provide accurate, context-aware answers across internal workflows.

What we built

We designed and deployed an internal AI assistant that acts as a centralized interface for accessing enterprise knowledge.

The solution included:

  • Integration with systems such as Jira, Confluence, and internal databases
  • A retrieval-augmented architecture combining structured and unstructured data
  • Natural language querying for accessing internal knowledge
  • Context-aware responses tailored to user roles and queries

The assistant was designed to support both technical and operational teams, providing fast access to relevant information across systems.

Impact in practice

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

Reduced time spent searching for information

by providing a single interface across multiple systems

More consistent and accessible knowledge

through centralized retrieval and structured responses

Improved productivity across teams

by reducing repeated queries and manual lookups

Better utilization of internal data and documentation

by making it easier to access and use existing knowledge

Technologies used

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

Generative AI
Natural Language Processing (NLP)
Python
Retrieval-Augmented Generation (RAG)
Vector databases

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