/ CASE STUDY 01
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Client projectAI Chatbot

Universitas Pembangunan Jaya AI Chatbot

University Marketing

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/ 01 — OVERVIEW
01

The problem before

Prospective students experience problems in obtaining admission information quickly due to limited operational hours, many repetitive questions, and information spread across various university sources.

02

What we did

Developing AI Chatbot based on Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) to automate university admissions information services. The system is equipped with FAQ Retrieval, knowledge base integration, and a web-based chatbot interface to help prospective students obtain registration information, study programs, tuition fees, and scholarships.

03

The result

AI Chatbot was successfully developed to provide admissions information services automatically 24/7, speed up access to information for prospective students, and help reduce repetitive questions that must be handled by admissions staff. The system also supports submitting answers based on official university information sources.

Development Jaya University AI Chatbot The development of the AI Chatbot at Pembangunan Jaya University (UPJ) was motivated by the need for fast, accurate and easily accessible admissions information services. Limited operational hours and the large number of repetitive questions are challenges in providing services to prospective students. As a solution, an AI Chatbot based on Large Language Model (LLM) and Retrieval-Augmented Generation (RAG) was developed which is able to answer questions automatically based on official university information. This system is equipped with FAQ Retrieval, knowledge base integration, and an administration dashboard to support information management. As a result, a web-based chatbot platform was created that allows prospective students to access registration information, study programs, tuition fees and scholarships 24 hours a day. This system is expected to increase service efficiency, reduce the workload of admissions staff, and provide a more modern information service experience.

Built with

  • Next.JS
  • Flask
  • REST API
  • Firebase Firestore
  • LLM
  • Selenium
  • React.JS

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