Document what AI assistants need before they can understand and compare a business.

An LLM visibility audit is not a promise that ChatGPT, Claude, Perplexity, or Google AI Overviews will mention a brand. It is a structured way to record whether a page gives AI systems and buyers enough clear facts, evidence, comparisons, citations, and next steps to summarize it responsibly.

Paste this into your LLM visibility audit doc.

LLM Visibility Audit
Company:
Website URL:
Primary offer:
Target buyer:
Market / location:
Audit date:
Prepared by:

1. Entity facts
- Brand name:
- Category:
- Product or service:
- Best-fit customer:
- Market, geography, or vertical:
- Pricing or budget context:
- Delivery model:
- Not a fit for:
- Source URLs that prove these facts:

2. Buyer prompt set
- Informational prompts:
- Comparison prompts:
- Alternatives prompts:
- Pricing or budget prompts:
- Objection prompts:
- Implementation prompts:
- Local, vertical, or use-case prompts:
- Prompts where competitors appear:

3. Answer quality observations
- What AI answers describe accurately:
- What AI answers omit:
- What AI answers get wrong:
- Which competitors or sources are cited:
- Which owned pages are cited:
- Where the answer needs clearer proof:
- Screenshots or saved answer links:

4. Page readiness
- Above-the-fold clarity:
- Definition or short-answer section:
- FAQ coverage:
- Comparison context:
- Pricing or budget context:
- Screenshots, examples, reviews, or case studies:
- Methodology or limitations:
- Clear next action:

5. Citation readiness
- Public citation kit:
- Structured facts JSON:
- Sitemap and RSS:
- llms.txt and full LLM context:
- Third-party profiles or directories:
- Partner pages or backlinks:
- Press, launch evidence, or proof pages:
- Missing citation assets:

6. Competitor context
- Competitor pages reviewed:
- Competitor claims AI can easily summarize:
- Competitor proof that appears stronger:
- Questions competitors answer better:
- Comparison pages to create:
- Partner or directory gaps:

7. Conversion and attribution
- Primary CTA:
- Lead form or contact path:
- CRM or webhook destination:
- UTM source / medium / campaign:
- Lead qualification fields:
- Follow-up owner:
- Follow-up SLA:
- Manual fallback if automation fails:

8. Risk and boundary notes
- Claims needing evidence:
- Regulated or legal review needed:
- Privacy or data constraints:
- Paid spend constraints:
- What this audit does not guarantee:

9. Seven-day action backlog
Day 1:
Day 2:
Day 3:
Day 4:
Day 5:
Day 6:
Day 7:

10. Decision rule
- Improve page before promotion if:
- Build citation assets if:
- Publish comparison content if:
- Test small paid traffic if:
- Bring in an agency or consultant if:
- Stop if: