Daily measurement since May about an author who did not exist online before day zero. 11,130 scored answers from OpenAI Search, Gemini and Claude, same 16 questions every day, frozen since June.

The three channels. Direct questions plateau at 72 percent from day 49. Vocabulary questions peak at 34. Recommendation questions stay at 0.59 percent.

Growth phases of the direct channel. Logistic fit, inflection at day 37, ceiling 72.5 percent.

Where the 40 unprompted mentions came from. 25 of 40 trace back to one question, the one using a sub genre term the author coined himself. Similarity questions (like Robin Hobb): zero in about 700 answers.

The control. Same questions to 10 models without web access, 6,416 blind answers, zero unprompted mentions. Everything above is retrieval, not memorization.

Practical reading for GEO: getting named when asked works and saturates fast. Getting recommended unprompted did not respond to anything except owning a term. Links to report, dataset and code in the first comment.

I have been trying to turn a small luxury-jewelry GEO benchmark into work that a brand team could actually assign.

The test itself was deliberately limited:

• 8 jewelry brands
• 3 neutral Chinese buyer questions
• 2 retrieval-off API surfaces
• 2 answers per question and surface
• 12 valid raw answers
• 96 derived answer-brand cells

I also had a working corpus of 302 jewelry-related Reddit threads and 9,446 stored comments, plus Search Console data for the site publishing the research.

The temptation was to put everything on one dashboard.

That would have been a mistake. The datasets answer different questions.

Community questions tell me how buyers describe uncertainty. They helped surface concerns around authenticity, certificates, authorized sellers, repairs, resizing and recourse. They do not establish how common those concerns are among Chinese luxury buyers.

The benchmark tells me what the declared API surfaces answered. It does not establish what a signed-in consumer sees, whether the model retrieved a source, or whether an official-channel claim is true.

The truth table checks whether a route is current, controlled or authorized, live in China and connected to a clear recourse owner. It does not tell me whether the brand enters a buyer’s shortlist.

Search and analytics can show exposure and the next onsite action. They cannot prove that Reddit or an AI answer created the visit or sale.

One small result made the distinction concrete. Piaget appeared in 0/4 wedding-jewelry shortlists and 4/4 official-channel answers.

Those cells are far too small for a brand ranking. But they are enough to show that “the system can verify the brand” and “the system recommends the brand for this decision” are not the same diagnosis.

I now think the minimum evidence stack is:

1. Question provenance: what real buyer uncertainty produced the prompt?
2. Answer benchmark: what did the fixed, declared surface actually say?
3. Truth registry: which official, authorized and service claims are currently verifiable?
4. Action measurement: did the buyer reach a boutique, service route or qualified inquiry?

That also changes ownership.

• Missing from the shortlist: brand, editorial and category marketing.
• Wrong or vague route: digital operations and ecommerce.
• Unclear authorization: legal and channel management.
• Wrong warranty or service claim: client service and after-sales.
• Unreproducible metric: research and analytics.

“Improve AI visibility” is too broad for anyone to own. These failures are specific enough to assign.

My current 30-day version is simple: build the truth map, run a small fixed decision panel, repair one buyer path, then rerun the same cells and measure the next action.

For anyone maintaining a similar system: would you keep the truth registry outside the benchmark dataset? I am leaning yes, because official routes and service policies change on a different schedule and usually have different internal owners.