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AI chat agent: The game-changer for telecoms

URN M24.0.713
Topics AI (Artificial Intelligence), Customer experience management, Customer lifecycle management

A new breed of chatbots which truly understand telco data

AI chatbots are becoming increasingly central to enterprise customer service efforts. When combined with large language models (LLMs), AI chatbots can be trained on datasets from diverse service areas, enabling them to provide swift, precise, and personalized assistance. This not only enhances the customer experience but also offers to drive significant revenue growth. The telecom sector, in particular, is ripe for adoption of such technology, using it to effectively discern customer intent and tailor services accordingly. This Catalyst seeks to create an AI-based customer service system which can provide the best possible user experiences across common user scenarios. The aim of the project is to engineer advanced conversational AI chatbots that can understand and use comprehensive telco-specific data to execute defined and targeted tasks, and engage interactively on demand with end users. The core architecture will integrate enhanced LLMs that have been fine-tuned with specialized telco datasets. The system will incorporate a hybrid framework to coordinate a rule-based system, natural language understanding (NLU) modules, and LLM technologies to ensure high performance and adaptability in complex business scenarios. The Catalyst seeks to show how LLM-based chatbots can determine consumer intentions and proactively resolve current issues, such as recommending personalized data plans, explaining products or initiating direct subscriptions. The outcome will be to improve telco customer satisfaction and reduce long-term overheads.

Resources

All you have to do is follow the AI journey we have prepared. Let's get started!

0. Start Here

0. Map for Our AI journey

1. Overview

1-1. Summary Infographic

1-2. Arena Speech Deck

1-3. Full Narrative Deck

1-4. Awards Submission

1-5. Showcase Video

2. Solutions

2-1. Reference Architecture

2-2. 3 Recipes for AI Success

2-3. TMF Standards Alignment

3. Use case & Demos

3-1. Use case and Architecture in Action

3-2. Working Demo: Scenario #1

3-3. Working Demo: Scenario #2

3-4. Working Demo: Scenario #3

3-5. Working Demo: Scenario #4

3-6. Working Demo: Scenario #5

3-7. Working Demo: Sustainability

4. Business Value

4-1. Business Value Summary

4-2. Business Value Analysis

4-3. Total Cost of Inferencing LLMs

5. References

GTAA Website

5-1. Innovation Catalysts Study

5-2. Enabling Sustainability

Contact team

Email the members of the Catalyst team to request more details.

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Email

Team members

Compax Software Development GmbH logo
Dell Technologies Inc. logo
MATRIXX Software logo
nvidia logo
OMANTEL logo
Champion
SK Inc. C&C logo
SK Telecom Co., Ltd. logo
Champion

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