WhoCites
AI Visibility Audit for Cursor-Built Apps
Cursor can accelerate product engineering, but distribution still depends on whether the public website explains the product clearly. WhoCites scans the live AI answers and turns visibility gaps into page-level fixes.
Run an AI visibility scanWhy this page exists
Cursor-built apps can have strong code and weak public positioning. If the homepage does not define the category, buyer, use case, and proof, AI assistants often choose better-described competitors.
What the scan checks
WhoCites runs one paid scan and turns the output into a practical visibility report.
- Buyer-intent prompts across 7 AI and search sources
- Brand rank and mention frequency
- Competitors named instead
- Citation and source-domain patterns
- Fix prompts for Cursor, Claude Code, or a developer
What the report includes
Each $49 scan includes one post-fix re-scan and practical recommendations.
- Cross-engine visibility score
- Per-engine mention table
- Competitor comparison
- Citation summary
- Prompted implementation tasks
- Included re-scan after fixes
Proof points
The product is intentionally small: one domain, one checkout, one report.
- Built for public marketing surfaces, not private code review.
- Best after the product has a real public URL.
- Gives engineering-friendly acceptance criteria.
Does WhoCites inspect my repository?
No. It scans the public URL and live AI/search answers, then gives implementation prompts you can use in Cursor or another coding tool.
Can a technically good app still be invisible?
Yes. AI visibility depends on public content, entity clarity, citations, and category signals, not only product quality.
What should I do before scanning?
Make sure the homepage is public and describes the app, audience, category, and pricing in plain language.