Monitoring

Multi-Model Mention Tracking and Competitor Gap Benchmarking

An LLM brand visibility tracker should show where you stand in the answer, not just whether a page exists. CiteUp samples the same buyer questions across global frontier and China mainstream AI assistants and reports the result per engine.

Brand Mention Rate and First-Recommendation Share

See how often the brand appears when a buyer asks for a shortlist, and how often it is the first brand the assistant recommends. Both are reported per engine so a gap in one assistant is not hidden by an average.

Same-Category Competitor Gap Comparison

Compare your mention rate with the competitors that appear in the same answers. The gap shows which buyer questions are being won by someone else, so the team can decide where to work first.

Attribution

Citation Source Tracing and Site Readability Probes

AI citation tracking answers a practical question: which pages did the assistant rely on? CiteUp lists the sources behind each answer and checks whether your own site is easy for AI crawlers to read.

Outdated Pages and High-Authority Source Identification

Find answers that still cite old product pages or third-party posts, and see which high-authority sources the assistants trust in your category. This tells you whether to fix your own site or earn coverage elsewhere.

AI Crawler Connectivity and First-Screen Fact Density Checks

Probe whether AI crawlers can reach your pages, and whether key product facts appear in the first screen of content that a crawler reads. Blocked crawlers and empty first screens are common reasons a brand is left out.

Fact verification

Five-State Verification Against Golden Enterprise Facts

Your team confirms a set of golden facts: product specifications, test reports, and credentials. CiteUp compares each AI answer with them and labels the result as consistent, partial, missing, conflicting, or outdated.

Spotting Conflicts, Omissions, and Outdated Parameters

A conflicting claim, a strength the assistant never mentions, and a parameter that was true last year are different problems with different fixes. Separate states keep them from being mixed together.

Fact-Anchored Drafting with Team Review

Drafts for FAQs, corrections, and buyer guides are built only from confirmed materials. A team member reviews each draft before it is exported or published. CiteUp does not invent claims or disparage competitors.

Retesting

Same-Baseline Batch Retesting and Effect Tracking

After content goes live, CiteUp reruns the same questions on the same engines so the new batch can be compared with the baseline. Answers from AI assistants change with the engine, time, and wording, so a like-for-like batch is the only fair way to see movement.

Results are observations from samples. CiteUp does not promise a ranking or a fixed improvement.

FAQ

Frequently Asked Questions

Which AI assistants does CiteUp monitor?

CiteUp covers 11 major AI assistants: ChatGPT, Claude, Perplexity, and Gemini globally, plus Doubao, Kimi, DeepSeek, Qwen, Hunyuan, Xiaohongshu Dots, and ERNIE Bot in China. The same list is used on the home page and in llms.txt.

What is the difference between mention rate and first-recommendation share?

Mention rate is how often your brand appears in answers to buyer questions. First-recommendation share is how often it is the first brand the assistant recommends. A brand can be mentioned often and still rarely come first.

Can CiteUp tell me which web pages an AI answer relied on?

Yes. Citation source tracing lists the pages behind each answer, so you can see whether it relied on an outdated page or a high-authority third-party source.

Does CiteUp write content automatically?

CiteUp drafts content only from facts your team has confirmed, and every draft needs human review before export or publication.