# Anonymized B2B AI Visibility Assessment

A bounded example for diagnosing gaps across technical language, product facts, buying questions, and public evidence.

- Evidence level: Anonymized diagnostic example
- Authorization boundary: No customer identity, logo, commercial data, or project outcome is disclosed. Only the reusable assessment structure remains.
- Updated: 2026-09-08

## Background
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 questions
- 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?

## Actions
- **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
- 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
- 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.

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- Canonical page: https://eraso.cn/en/cases/b2b-visibility-review/
- Brand facts: https://eraso.cn/en/brand/
- LLM index: https://eraso.cn/en/llms.txt