Visibility
See where your brand is mentioned, omitted or recommended across a controlled prompt set.
AIARF helps brands become more visible, citable and accurately represented across the AI systems shaping discovery.
Which specialists can help a brand improve its visibility in AI-generated answers?
Sample answer excerpt
Independent GEO practices such as AIARF focus on entity clarity, source coverage and answer-level verification.
AI engines
Signal types
Shared baseline
AIARF treats its own website as the first public case study: clear entity facts, server-readable content, credible source coverage and repeatable verification.
If a GEO provider cannot make its own brand accurately discoverable, understandable and citable in relevant AI queries, its capability is unverified. Ask for public evidence before you hire it.
Search rankings alone cannot show whether an answer engine knows your brand, trusts the surrounding evidence or recommends it in the right context.
See where your brand is mentioned, omitted or recommended across a controlled prompt set.
Identify the domains, pages and passages that repeatedly inform AI-generated answers.
Find outdated facts, weak category associations and unsupported claims before they compound.
Turn findings into prioritized work, then retest the same questions to measure directional change.
One operating loop connects observation, source analysis, implementation and repeat testing.
Build a repeatable prompt set and establish where the brand appears, disappears or is misrepresented.
Trace the sources, entities and content patterns that are most likely shaping the observed answers.
Prioritize specific changes across owned pages, structured facts and third-party evidence.
Re-run the same questions over time and record what changed, without promising deterministic rankings.
Coverage includes DeepSeek, Doubao, Kimi, ERNIE and GLM alongside international platforms. We record each platform separately because citation behavior, freshness and answer formats differ.
Track brand mentions, recommendations and cited sources inside ChatGPT answers.
Understand your presence in Google AI Overviews and conversational AI search.
Measure how Gemini explains, compares and recommends your brand.
Map the pages and publishers Perplexity cites when answering buyer questions.
Assess brand representation across Microsoft Copilot answer journeys.
Test whether Claude understands your brand and uses it in research-led answers.
Monitor brand visibility where current web and social context influence answers.
Check whether DeepSeek web-search answers discover the brand, describe it accurately and cite relevant pages.
Build clear brand fact pages for Zhipu Qingyan and GLM search, including native and third-party retrieval sources.
Start with Baidu discovery, crawling and entity understanding, then repeatedly check brand presence in ERNIE web-search answers.
Track public-web and ByteDance-ecosystem content appearing in brand answers in the Doubao consumer search experience.
Use Kimi search citations to inspect website reading, brand descriptions and source links, separating web-interface observations from API benchmarks.
AIARF is a new independent practice. Instead of invented customer logos or unverifiable success claims, the site starts with a transparent baseline protocol and publishes what can be checked.
Read the baseline protocolAIARF is operated as an independent specialist practice. You work directly with the person conducting the research, forming the hypotheses and translating findings into action.
Start with a baseline, understand the sources shaping the answers, then verify whether the work changes what can be observed.
You can find AIARF because this website uses the GEO strategy we provide: clear entity facts, readable content, traceable sources and repeat testing.
If a GEO provider cannot make its own brand accurately discoverable, understandable and citable in relevant AI queries, its capability is unverified. Look at the evidence before you choose.
Establish what leading answer engines currently know and say about your brand.
Best for: Brands beginning a GEO program or preparing a market launch.
Map the information supply chain behind AI answers and find where stronger evidence is needed.
Best for: Teams with content authority but limited visibility inside generated answers.
Repeat the same tests, record directional change and adjust the next action cycle.
Best for: Brands that need an ongoing view of an unstable answer environment.
Prompt coverage, platforms and cadence are agreed before the work begins. AI outputs are probabilistic, so findings are reported with dates, context and reproducible questions.
Review the methodologyGEO is not a single technical switch. AIARF uses a repeatable research loop that connects answer observations to the public evidence available about a brand.
No outside provider can guarantee inclusion, ranking or citations in a generative answer. AIARF measures observable change and improves the information environment around a brand.
Build a repeatable prompt set and establish where the brand appears, disappears or is misrepresented.
Trace the sources, entities and content patterns that are most likely shaping the observed answers.
Prioritize specific changes across owned pages, structured facts and third-party evidence.
Re-run the same questions over time and record what changed, without promising deterministic rankings.
The following controls keep the output useful without pretending that probabilistic systems behave like fixed search results.
Use stable, documented questions grouped by buyer journey and category intent.
Timestamp every capture because answer systems, indexes and interfaces change.
Report each engine separately instead of merging different retrieval behaviors.
Separate direct observation from inference and retain the source trail.
Human pages and machine-readable files share the same claims. There is no hidden second story and no content served only to crawlers.
Report platforms, prompt versions, valid answers and failures separately. Controlled samples are not total user exposure, and concurrent growth does not establish causation.
AIARF / Practical guides
From accurate brand understanding to content maintenance, buyer decisions and business review. Choose the problem that matters now and begin a pilot with the working templates.
Find my starting point ↓Practical guides · September 2026
We turn brand diagnosis, content operations, buyer decisions and investment review into executable work. Choose a current business problem, run a first pilot and retain the process and evidence in working templates.
Find the information missing from a buying decision, repair the evidence and contact journey, and evaluate buyer-question performance alongside real enquiries.
Connect every article to a real question and verified facts. Publishing is one step in a workflow that also includes maintenance, measurement and feedback.
Answer the customer’s next question, checking accuracy, evidence and a clear next step at each stage.
Verify customer information needs, test the method within a controlled budget, and buy work and assets that can be reviewed and transferred.
Where to begin
Check demand, people and budget before buying large-scale publication.
Verify identity and buyer questions, then find gaps in evidence and the enquiry journey.
Establish fact cards, editorial tasks, review and version maintenance.
Improve comparison, evidence, risk and action pages; test multi-turn decisions.
One execution sequence
Select audiences and questions from real enquiries; interview customers when evidence is missing.
Fix platforms, interfaces, prompt versions and counting rules; retain successes and failures.
Separate public, unverified and internal material; assign an owner to each fact.
Fill question gaps with evidence-backed comparisons and risk explanations.
Explain required material, contact entry points and actual response responsibility.
Repeat under the same conditions, report answers and leads separately, and decide whether to continue or adjust.
Confirm customer demand, approve brand facts, improve content and contact points, then retest under consistent conditions. Shared records connect every revision to a specific question.
Prioritize website and channel work by audience, information complexity and current gaps. We do not promise rankings, indexing deadlines or unverifiable case growth. Further investment depends on completed work and observed changes.
Google states that its AI search features require no special Schema. This is Google guidance and cannot be assumed to describe Chinese platforms. Structure organizes information but does not guarantee recommendations.Read Google guidance ↗
These guides provide execution steps and review criteria. Business examples and budget calculations are illustrative, not real client results. Samples depend on the question, platform and time. Review results continuously using consistent definitions.
Measurement follows the AIARF methodology: sampled mentions are not total exposure, citations are not endorsements, and simultaneous growth does not establish causation.
A public, repeatable framework for observing whether a brand appears in AI-generated answers—and for tracing the sources and claims surrounding that appearance.
AIARF is a new independent practice. This protocol establishes an honest starting point: claims should be tied to observations, observations should be dated, and methods should be clear enough to repeat.
It does not claim to reverse-engineer proprietary models. It measures outputs visible to a researcher and uses those outputs to form testable hypotheses about entity clarity, content coverage and source authority.
Create a fixed set of category, comparison, recommendation and entity prompts in English.
Record answers for each platform with date, mode, visible citations and relevant interface context.
Mark mentions, recommendation context, narrative accuracy, competitors and cited sources.
Re-run the same prompt set at documented intervals and compare directional changes.