Work → Case Studies → Wellness Buddy Thailand — LLM-Powered Wellness Recommender
Wellness Buddy Thailand — LLM-Powered Wellness Recommender
I built Wellness Buddy for a simple reason: Thailand's wellness market is worth billions, but travelers still struggle to choose with confidence.
Overview
I built Wellness Buddy for a simple reason: Thailand's wellness market is worth billions, but travelers still struggle to choose with confidence.
Role: AI Product Developer & Researcher
Timeline: 2024–Present
Tools: Python · NLP · LLMs · Next.js
The Problem
Big Market. Confused Buyer.
Thailand is one of the world's wellness hotspots. The market is worth billions. But for most people, choosing a program still felt like guesswork.
I kept seeing the same pattern: travelers were not choosing the best option. They were choosing the safest-looking one. The one that felt easy to justify.
Meanwhile, 430+ wellness programs were described inconsistently, and 6,400+ reviews sat untouched. The signal was there. The product gap was obvious.
Research
Listen to Behavior, Not Hype
- I analyzed 6,400+ traveler reviews from Google Maps and TripAdvisor across 165 wellness centers
- I mapped language patterns into 6 wellness dimensions: physical, mental, spiritual, emotional, social, environmental
- I found a recurring behavior: travelers describe goals in human terms, but providers market in category terms
- I published the findings in IEEE ICSEC 2024
Solution & Build
From Research to Real Product
- I built an NLP pipeline to classify reviews and extract sentiment by wellness dimension
- I trained an intent-matching model with 90% classification accuracy
- I added an LLM layer to explain recommendations in plain language people can trust
- I published extended research in IEICE Transactions 2025
- I shipped wellnessbuddythailand.com as a live production product
Outcome
✓
Live product serving international wellness travelers in Thailand
✓
2 peer-reviewed publications tied directly to shipped product decisions
✓
End-to-end research-to-product execution completed as MSc thesis
✓
Closed a real decision gap in a multi-billion-dollar tourism category
Learnings
I learned that people do not buy the most accurate recommendation. They buy the one they understand in 10 seconds. Accuracy builds the engine. Trust drives the click.