Case 03
↳ stretched support
Can AI Distinguish a Branch From the Business as a Whole?
Read → A repeatable-run study of whether branch labels, addresses, operating details, and sources remain attached to the correct Thai location.
Research record
Cases from Thai AI search
The index contains investigations of how generated answers represent Thai businesses, brands, and places. Materials are organised around recurring failures in retrieval, identification, attribution, citation, and wording. Each case preserves the answer under study and follows its claims back toward the available evidence. New work is published when a case has been repeated, checked, and described clearly enough for another reader to inspect the reasoning.
14 cases · 3 directions · th, en
Direction I
Entity identity and naming
How AI search systems connect a prompt to a particular Thai business or place. Concentrates on English transliteration variants, shared names, branch labels, platform categories, and local geographic wording. The materials separate retrieval path from entity identification.
Case 02
⤳ borrowed identity
When Do Shared Names Produce a Mismatched Recommendation?
Read → An investigation of how similarly named Thai venues exchange categories, locations, branch details, and reputation claims in AI recommendations.
Case 04
⤳ borrowed identity
When Is a Thai Place Assigned to the Wrong Province?
Read → A close examination of how correct-looking Thai place names become attached to incompatible districts, cities, and provinces in generated answers.
Case 01
⤳ borrowed identity
Which Thai Business Name Does AI Choose After Transliteration Drift
Read → A study of how AI search systems select among competing English spellings of a Thai business name and which transliteration earns the citation when records diverge.
Direction II
Claims and citation support
The relationship between individual claims in generated answers and the visible material associated with them. Each investigation asks separately what a source establishes about the entity, category, address, ownership, recommendation, or quality judgment.
Case 13
✓ direct support
Which Thai page elements make AI cite the business
Read → A close look at how Thai names, address wording and category cues help AI answers attach a business to its own page instead of nearby evidence.
Case 06
— unsupported arrival
When Does an Old Listing Reappear in a Current Answer?
Read → A study of how former addresses, names, services, and operating details return as current claims through still-retrievable business listings.
Case 05
↳ stretched support
Does the Citation Support the Business Category?
Read → An investigation of citations that establish a Thai business and its services while failing to support the category assigned in an AI answer.
Case 08
— unsupported arrival
When Do Recommendation Claims Appear Without Visible Support?
Read → An examination of praise, ranking language, and recommendations that exceed what the visible sources establish about a Thai business.
Case 07
⤳ borrowed identity
When Does a Source Transfer Another Entity’s Identity?
Read → A close examination of how a relevant-looking page can lend another business’s category, location, or reputation to a generated answer.
Direction III
Variation across runs and systems
Whether an entity identification or claim-source relationship persists when the prompt language, wording, model, or observation occasion changes. Agreement across systems is not treated automatically as confirmation.
Case 14
§ method
How Thai businesses can self check AI visibility errors
Read → A practical method for recording AI-search errors without specialist tools, using prompts, visible citations, dates and mismatch types.
Case 12
↳ stretched support
How Does Prompt Wording Change the Apparent Retrieval Path?
Read → Small changes in service, location, comparison, and recommendation wording can redirect entity selection, citations, and the claims built from them.
Case 09
⤳ borrowed identity
Does Asking in Thai Produce a Different Entity Identification?
Read → Matched Thai and English prompts reveal how language can alter the business, branch, category, location, and sources assembled into an answer.
Case 11
⤳ borrowed identity
When Do Several Models Repeat the Same Mistake?
Read → Cross-model agreement can reveal a shared public-source ambiguity, yet identical errors remain observations rather than independent confirmation.
Case 10
— unsupported arrival
Which Errors Return Across Repeated Prompt Runs?
Read → A repeated-run study separates harmless wording variation from entity, location, attribution, and citation errors that return under preserved conditions.
Start with the answer, then follow its sources.
Browse the record by entity problem, discovery situation, or source relationship.
About the method →