Đồ án tốt nghiệp - Developing AI-Powered lead qualification call system

MINISTRY OF EDUCATION AND TRAINING
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY OF INTERNATIONAL EDUCATIONG

CAPSTONE PROJECT:
DEVELOPING AI-POWERED LEAD QUALIFICATION CALL SYSTEM

ADVISOR: M.Eng. Nguyen Dang Quang
CO-ADVISOR: Mr. Vo Cao Son
EXAMINER: MSc. Nguyen Duc Khoan
STUDENTS:
Tran Binh Phuoc

21110079

Tran Duc Quang

21110793

Ho Chi Minh, May 2025

MINISTRY OF EDUCATION AND TRAINING
HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION
FACULTY OF INTERNATIONAL EDUCATION

CAPSTONE PROJECT:
DEVELOPING AI-POWERED LEAD QUALIFICATION CALL SYSTEM

ADVISOR: M.Eng. Nguyen Dang Quang
CO-ADVISOR: Mr. Vo Cao Son
EXAMINER: MSc. Nguyen Duc Khoan
STUDENTS:
Tran Binh Phuoc

21110079

Tran Duc Quang

21110793

Ho Chi Minh, May 2025

ACKNOWLEDGEMENT
We would like to sincerely thank M.Eng. Nguyen Dang Quang from the Faculty
of International Education for his strong support and helpful guidance during our
project. His advice and feedback helped us solve problems, improve our work, and stay
on the right track. We truly appreciate his time and dedication to helping us succeed.
We are also very thankful to our Co-Advisor, Mr. Vo Cao Son, for his kind
support and useful suggestions throughout the project. His technical knowledge and
clear advice helped us better understand many parts of our work. His support made a
big difference, and we are grateful for his efforts.
I would also like to convey our heartfelt appreciation to the esteemed professors
at Ho Chi Minh City University of Technology and Education, particularly the faculty
members, for their enthusiastic teaching and the extensive knowledge and expertise they
shared with us during our academic journey. Their support has been instrumental in
enabling me to successfully complete this project.
Nonetheless, I acknowledge that errors may have occurred during the course of
this project’s implementation. I respectfully invite constructive feedback from my
instructors, as it will provide valuable insights to enhance my learning and professional
skills, ultimately contributing to my future endeavors in both education and the
workplace.
Implementation Group
Tran Binh Phuoc

21110079

Tran Duc Quang

21110793

TABLE OF CONTENTS
CHAPTER 1.

INTRODUCTION ............................................................................... 1

1.1.

Rationale for the Project ................................................................................ 1

1.2.

Objectives of the Proj
...

...
t options.
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Restricted Data Processing Capabilities: The system does not offer advanced

analytics or extended data enrichment features beyond initial qualification.
6.3.

Future Development Plan

Several areas offer potential for future improvement and expansion:
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Custom AI Model Support: Enable users to upload or fine-tune their own

models for domain-specific lead qualification.
-

Advanced Analytics Dashboard: Integrate detailed analytics and insights into

pipeline performance, lead conversion rates, and AI accuracy.
-

Subion Management Module: Introduce billing integration to support

SaaS deployment and commercial scalability.
-

Multi-Language NLP Capabilities: Expand AI modules to support multiple

languages for broader global reach.
-

Omnichannel Support: Extend lead sources and communication channels (e.g.,
WhatsApp, LinkedIn, SMS) for enhanced lead engagement.

-

Multi-Language NLP Capabilities: Expand AI modules to support multiple
languages for broader global reach.

104

REFERENCES
1.

Klemm, N. (2025). Ultimate Guide to Automated Lead Qualification in 2025:
Methods and Frameworks. Synthflow AI.

2.

Frontiers in Artificial Intelligence. (2025). A Review on AI-Based Business
Lead Generation and a Case Study.

3.

SalesTech Star. (2024). AI-Powered Lead Qualification: Key Benefits for Sales.

4.

IBM. (2025). AI for Lead Generation.

5.

B2B Rocket. (2025). The Future of Automated Lead Qualification: Combining
AI with Human Insight.

6.

OpenAI. (2023). OpenAI API documentation. https://platform.openai.com/docs

7.

Microsoft Azure. (2023). Azure Cognitive Services - Speech service
documentation. https://learn.microsoft.com/en-us/azure/cognitiveservices/speech-service

8.

Roy, G. M. (2023). RabbitMQ in Depth. Manning Publications.
https://www.manning.com/books/rabbitmq-in-depth

9.

Chodorow, K., &amp Dirolf, M. (2013). MongoDB: The definitive guide. OReilly
Media. https://www.oreilly.com/library/view/mongodb-thedefinitive/9781449344795/

10. Solem, A. (2023). Celery documentation. https://docs.celeryq.dev/en/stable/
11. xyflow team. (2023). React Flow documentation. https://reactflow.dev/docsss
12. Silbermann, S., Cogswell, R., Lee, Y., Alves Lima, J., Shen, D., &amp Tamboli, Z.
(2023). Material UI documentation. https://mui.com/material-ui
13. Demers, C. (2023). dnd-kit documentation. https://docs.dndkit.com
14. CrewAI
Inc.
(2024).
CrewAI
https://docs.crewai.com/introduction

documentation.

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