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AI search research

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Methods2026-10-07

Same engine, different surface, different answer: test app, web and API separately

On the same platform and the same questions, app and web cite systematically different source sets — and the API shows something else again.

Chinese generative search: four mainstream platforms across web and app — eight interfaces, 614 queries, three replications per combination, cleaned from 214,119 raw records into 160,860 citation-level records; one conclusion is that app and web source sets differ systematically for the same platform.

Key numbers · 4References · 3Read →
Methods2026-10-07

How to design a retest you can trust: five moves so noise does not pass as progress

AI visibility is a distribution, not a point: fix the prompts, repeat the runs, add controls, paraphrase, validate by hand — or you cannot attribute the change.

Why repeats are mandatory: one GEO measurement study concludes answers vary across runs, prompts and time, making one-off observations unreliable and visibility a distribution; a brand-recommendation evaluation study likewise requires repeated sampling because the same query yields different brands and rankings.

Key numbers · 4References · 3Read →
Methods2026-10-07

What GEO methods actually hold up: 45 studies and a cold shower

The widely cited “40% visibility lift” comes with a condition: the source was already in a fixed context. It proves neither discoverability nor durable traffic.

Scope: 45 studies published between November 2023 and July 2026 (plus one earlier preprint accepted at EMNLP after the window opened), together with related RAG and evaluation work.

Key numbers · 5References · 1Read →
Methods2026-10-06

How to evaluate a conversational AI system: 23 metrics plus agreement with human raters

Single-turn, single-task accuracy cannot capture real use; multi-turn dialogue needs five dimensions, 23 metrics — and proof that machine scoring matches human scoring.

Framework: derived using the Goal-Question-Metric method across five categories — task performance, factual accuracy, interaction quality, control capability and system efficiency.

Key numbers · 4References · 1Read →
Methods2026-10-06

How to build a per-market source list: see who a market reads before deciding what to fix

The top cited source differs by market — YouTube in Poland, a local business daily in Lithuania — and Chinese platforms differ between app and web.

Cross-market evidence: 128 brands, 12 home markets, 13 languages and 167,551 URL-grounded citations — Wikipedia was the most-cited domain in 11 of 12 languages, the exception being Lithuanian, where the business daily vz.lt led at 4.38%.

Key numbers · 4References · 2Read →
Methods2026-10-05

How to retest off-site coverage: register, re-run the same prompts, compare the source pool

Off-site work is not finished when you publish: re-run the same prompts, check whether the cited source pool changed, and whether the new sources brought discoverability.

Registration has quantified value: across 200 synthetic disruption episodes, registering offers in the same index lifted captured value from 46.8% to 98.6% — unregistered capacity caused requests to fail outright.

Key numbers · 4References · 3Read →
Methods2026-10-05

Small businesses can run their own Q&A assistant too: low-cost multi-tenancy and injection defence

For small teams the hard part is not model choice but satisfying isolation, cost and security at the same time.

Setting: an open-source multi-tenant platform that lets small businesses deploy customised LLM support chatbots through a no-code workflow, where the obstacles are infrastructure cost, engineering complexity and RAG-specific security risk.

Key numbers · 5References · 1Read →
Methods2026-10-05

Stop collapsing visibility into one score: separate discoverability, citation, absorption and revenue

A single “AI visibility score” hides four different links in the chain: being retrieved, being cited, being absorbed into an answer, and producing business.

The survey decomposes GEO into a multistage pipeline: search activation → crawling and indexing → retrieval → reranking and context allocation → citation → prominence → factual absorption → fidelity → user behaviour.

Key numbers · 4References · 1Read →
MethodsE-commerce2026-10-05

Can AI actually read your product pages? A 3,000-page extraction experiment

What AI needs from a product page is not pretty layout but extractable structured facts: specs, ingredients, price, certification.

Scope: 3,000 food product pages from three online retailers, with the goal of extracting key attributes such as ingredient lists and nutrition tables.

Key numbers · 4References · 1Read →
Methods2026-10-04

How to test three languages: run Chinese, Traditional Chinese and English separately

Cross-lingual consistency research shows answers change with language, so multilingual markets must be asked and measured language by language.

Finding one: multilingual models frequently answer semantically equivalent questions inconsistently, which is why cross-lingual consistency (CLC) is its own field; direct distribution alignment in post-training improved consistency across three model families and three benchmarks.

Key numbers · 4References · 3Read →
Methods2026-10-04

Why one measurement is not a measurement: AI visibility is a distribution

Ask the same question twice and the brands and rankings change — a single observation cannot tell you where you stand.

A study on measuring GEO concludes answers vary across runs, prompts and time, making one-off observations unreliable: brand visibility should be characterised as a distribution, not a single point.

Key numbers · 4References · 2Read →