How to Run an AI Search Visibility Audit

An AI search visibility audit establishes a quantifiable baseline: for core business queries, how often your brand is cited vs. key competitors, which existing content AI already retrieves, and which topics haven't entered the answer pool.

Recommended scope: Doubao, DeepSeek, Yuanbao, and major domestic AI search products; 15–30 queries from sales, product, and support covering brand, category, solution, and comparison terms.

A useful audit should answer more than "are we mentioned?" It should explain why competitors are cited, which of your assets AI fails to retrieve, and which queries deserve priority content investment. The first answer defines the competitor gap; the latter two define the 90-day content roadmap.

Queries should mimic real customer questions, not just brand searches. Examples: "What factors matter when selecting industrial MES?" "What are mainstream digital transformation options in industry X?" "Compare Company A vs. B for product Y." Repeat each query 2–3 times per platform (answers vary); record whether your brand, competitors, and source URLs appear.

Suggested fields: platform, query, test date, brand cited (Y/N), competitors cited, answer summary, suspected source URL. Use spreadsheets or a lightweight database for comparison over time.

Group queries into four types: brand queries for entity recognition, category queries for baseline visibility, scenario or solution queries for solution authority, and comparison or selection queries for high-intent conversion opportunities. If resources are limited, prioritize the last two because they sit closer to purchase preference formation.

Baseline metrics include: brand citation rate (queries citing you / total queries), competitor citation rate, gap vs. top competitors, and share of queries with no authoritative answer (vague AI responses citing no brand).

Also track which site pages, whitepapers, or FAQs appear as AI sources, and which high-quality assets AI hasn't adopted—often pointing to structure or clarity gaps.

A minimal template can include ten fields: platform, query, query type, test round, brand mentioned, mention position, competitor names, suspected source URL, whether the answer contains a decision recommendation, and next content action. This turns audit output into publishing tasks instead of passive observation.

If brand citation is below 20% while competitors exceed 50%, prioritize knowledge content for high-intent queries. If you're mentioned but misattributed, strengthen brand entity and product Schema. If entire query categories lack authoritative answers, that's a thought-leadership window.

The audit report should output: a prioritized content list, competitor gap analysis, and a 90-day actionable roadmap. Score topics by business impact, current citation gap, and content production difficulty: high-impact, high-gap, low-difficulty topics enter P0; high-impact but harder topics enter P1; long-tail education topics enter P2.

After the audit, avoid jumping straight into generic blog production. A stronger first move is building 5–10 excerpt-ready knowledge units: core FAQs, selection guides, case summaries, comparison explanations, and structured service pages. These assets test whether AI is willing to cite your brand content faster.