BO AN CHEN
陳柏安

Intro

Hi, I'm Bo An Chen (陳柏安).
I'm a student majoring in Information Management at National Taiwan University of Science and Technology.

I enjoy using technology to make things simpler and more intuitive.

Whether it's building an app, integrating AI, or designing a system workflow, what matters most to me is that the people using it find it helpful and natural.

I believe great products come from understanding people's needs — not just what's technically possible, but what truly feels right to use. That's what I care about most in every project I take on.

Feel free to look around and learn more about my story.

• View My Projects

Work

Here are some of my featured projects. Click on any project to learn more.

WhatEat APP

AI-powered restaurant recommendation app

KM_CSS

Enterprise customer service repair management system

WhatEat APP

AI-Powered Restaurant Recommendation

WhatEat is a university capstone project that solves the "what to eat" decision paralysis problem. Unlike traditional food apps that rely on keyword searches, WhatEat uses AI to proactively recommend restaurants based on your implicit needs.

Key Features

• Contextual Understanding: Uses LLM to understand implicit needs (mood, weather, dining purpose)
• Explainable AI: Tells users WHY a restaurant is recommended, building trust
• Smart Search: AI-powered conversational search interface
• Personalization: Dietary preferences and restrictions support

System Architecture

A hybrid RAG (Retrieval-Augmented Generation) architecture combining SQL Server and Vector Database.

• Intent Analysis: LLM parses natural language into structural JSON constraints.
• Retrieval: Fetches candidates via SQL (User Data) and Google Places API.
• Personalization: Re-ranks results based on historical preferences and weighted features.
• Generation: LLM synthesizes the final answer with reasoning based on retrieved data.

Technical Highlights

• Anti-Hallucination: Strictly constrained RAG allows the AI to only recommend real, existing restaurants.
• Hybrid Analysis: Merges Unstructured Data (Reviews) with Structured Statistics for precise personalization.
• Dynamic Context: Adapts to GPS location, time of day (Lunch/Dinner), and current weather.

Screenshots

Conversational Search

Visual Map Selection

AI Recommendations

Reasoning & Details

Rating & Feedback

Personal Preferences

Tech Stack

Android (Kotlin), LLM API, Google Places API, SQL Database

Team

BO-AN CHEN (陳柏安) - Full Stack Development
Yi-Hao Dong (董亦浩) - @kivxxx

• ← Back to Work

KM_CSS

Customer Satisfaction Survey & Service Management System

KM_CSS is an enterprise-grade customer satisfaction survey and service management system developed during my internship at KENMEC. The system enables end-to-end management of customer satisfaction surveys, service tracking, engineering progress monitoring, and data-driven analytics — all through a modern dark-themed admin dashboard.

Key Features

• Analytics Dashboard: Real-time overview with KPI cards, Chart.js visualizations (bar charts, doughnut charts), and latest feedback display
• Survey Management: Token-based survey link generation, customizable survey questions, and customer satisfaction data collection
• Engineering Progress: Track maintenance and renovation projects with ERP data synchronization, quotation tracking, and status management
• Customer & Employee Management: Full CRUD operations for customer and employee records, employee-customer mapping, and shift scheduling
• Satisfaction Analysis: Per-customer satisfaction trends, score distribution analysis, service type statistics, and time-filtered detailed reports
• Service Request Portal: Customer-facing forms for submitting service requests with contact info, problem descriptions, and photo uploads
• Employee KPI: Automated KPI calculation based on customer satisfaction scores linked to assigned technicians
• AI Service Report Analysis: Integrated LLM-powered analysis that automatically grades service request severity (Emergency / Medium / Normal) and generates concise AI summaries for rapid triage

Security Implementation

• Multi-level RBAC: Session-based authentication with 3-tier permission levels — Admin (Level 2), Department Member (Level 1), and Guest (Level 0)
• Token-based Access: Unique cryptographic tokens for each customer survey link with expiration and activation controls
• Input Validation: Server-side strict validation on all API endpoints to prevent SQL Injection and XSS attacks

Tech Stack

Flask (Python), SQLite, Vue.js 3, Element Plus, Chart.js, Groq AI (LLM)

My Role

Full Stack Developer (Internship Project)

Screenshots

Admin Dashboard

Engineering Progress

Customer Statistics

Satisfaction Analysis

Service Statistics

AI Service Report Analysis

Admin Login

• ← Back to Work

Experience

About

Skills

• Programming: Python, Kotlin, Dart, JavaScript, SQL
• Frameworks & Tools: Flutter, Vue, Android Development, Git, VS Code
• Databases: MySQL, SQLite
• Other: RESTful API, LLM Integration

Education

National Taiwan University of Science and Technology
B.S. in Information Management (Class of 2026)
Relevant Coursework: Database Management, Software Engineering, Capstone Project

Interests

• Travel and exploring new places
• Researching tools and automation
• Gaming, Anime, Fitness, Web3

Goals

• Short-term: Complete my degree and gain hands-on industry experience
• Long-term: Deepen expertise in Automation, Quantitative Trading, and Web3

Personal Traits

• Detail-oriented with a passion for efficiency
• Enjoy solving complex problems with elegant solutions
• Skilled communicator who bridges technical and non-technical perspectives
• Strong team player with experience in collaborative projects

• Download Resume

Contact

• GitHub
• LinkedIn
• Twitter
• Instagram