Example observation window: a fixed question set retested over seven consecutive days
Anonymized diagnostic example
Anonymized B2B AI Visibility Assessment
A bounded example for diagnosing gaps across technical language, product facts, buying questions, and public evidence.
Context
Background and decision question
A B2B company with an existing website, product documents, and sales team wants to understand why its brand rarely appears when buyers ask AI systems about suppliers, specifications, and applications. This example explains the assessment logic and does not represent a completed engagement.
Baseline
Baseline design
- Major China AI answer platforms
- Major global AI search platforms
- Which suppliers operate in this category?
- Which products fit a stated operating or procurement condition?
- How should buyers compare specifications, certifications, and delivery capability?
- Which company operates the brand and what does it sell?
- Which verifiable market or delivery evidence is public?
- Official website and public product documents
- Accessible sources cited in platform answers
- Public industry bodies, standards, and media records
Actions
Actions in the example
Align entity facts
Check the company, brand, products, models, capabilities, and markets for missing or conflicting facts.
Build a buying-question set
Use sales conversations to define discovery, comparison, validation, and action questions.
Inspect evidence pages
Verify that specifications, credentials, boundaries, and delivery capability have stable, crawlable pages.
Create a retest log
Retain the question, platform, date, answer, citations, competitors, and factual accuracy for repeatable comparison.
Verification
How change would be verified
- Can the system state the brand identity and core business accurately?
- Are important product facts supported by owned or credible public sources?
- Do comparison answers include fit conditions and evidence?
- Do mention, citation, and accuracy signals change repeatedly under the same test set?
Limitations
What this example cannot prove
- The example does not disclose a customer, original answer samples, or commercial outcomes.
- Answers vary by time, account, market, and context; one screenshot does not establish stable performance.
- Recommendation still depends on offering quality, public evidence, and fit with the question.
Deliverable excerpt
Inspectable output
Illustrative deliverable excerpt
A formal assessment breaks observations into traceable records instead of presenting one composite score.
- Question and platform sample table
- Entity and fact conflict register
- Evidence-page priorities
- Fixed-question retest plan
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