Canlah Research
How AI picks its answers, measured in the open
Canlah Research is the research arm of Canlah AI, a Singapore GEO agency. We publish studies, not opinion posts: raw data and rerunnable methods with every release, conflicts disclosed in place, losses published with the wins.
Publications
-
2026-08-30 · Case study · open data
Same Question, Seven Engines
One commercial question, seven search/answer engines, one night: no page cited by 5 or more engines, mean top-10 overlap 0.171 (CN) / 0.091 (EN). Per-engine source profiles, targeting hypotheses, and a logged-out ChatGPT replication.
-
2026-08-21 · Open dataset · DOI
Agent-Readiness of 50 Cross-Border DTC Brands (2026)
25 of 47 reachable storefronts served a UCP manifest — and all 25 were issued by Shopify, none by the brand. Agent-readiness, today, is a platform switch, not a brand decision.
-
2026-08-11 · Whitepaper · free to read
Marketing in the Agent Era
When AI becomes the first reader and the first buyer: 28 chapters, free to read in full, built on the original dataset above.
How we work
- Questions come from practice: every study starts as something we actually hit while doing GEO work, not a content calendar.
- Data before opinion: a release means raw data + probe specs + classification code, so every derived statistic can be recomputed without trusting us.
- Limits in the body text: how small n is, what is hypothesis, what went unverified — you should meet a number's limits before you can quote it.
- Conflicts disclosed in place: we are a GEO vendor measuring the market we participate in; our own brand's failed measurements are published with the wins.
Corrections, replications, collaboration: admin@canlah.ai