AI trust begins with a clinic it can name correctly.
A patient asks an assistant where to get dental care in Thailand, and the answer sounds confident. The clinic name may be slightly wrong, the district too broad, the service label borrowed from reviews, or the treatment list copied from an old profile. In this free mini-course, I show independent Thai dental clinics how to read those answers carefully and make their public evidence easier for AI systems to name, locate, classify, and cite.
What the course studies
The course follows the full path from one patient-style question to a monthly clinic self-audit. Across 14 lectures, we look at Thai and English clinic names, transliteration, districts and provinces, service categories, treatment evidence, public sources, reviews, bilingual pages, and repair work. Each lecture ends with a small patient-answer reading: name used, place assigned, service inferred, source borrowed. It is built for clinic owners, managers, coordinators, and communication staff who already understand dental services and patient flow. The material is free, practical, and without obligation. You do not need SEO software, coding, or language-model expertise; you need access to the clinic’s public evidence and the patience to read an AI answer line by line.
- 14 lectures
- 6 tracks
- Free tuition
Mark how the answer reached the clinic — name, place, service, or source — or where it borrowed.
The lectures are designed to be read in order, because each one adds a new part of the visibility record. Later, the index will also let you return to a single problem: a name mismatch, a district error, a weak treatment claim, or a review fragment that has started to sound like evidence.
Make the clinic easier to recognize before asking AI to recommend it.
Start with one answer, then trace the name, place, service, and source behind it.