[unCited]
ProductCitation IndexAI InfluenceBlogBook
[unCited]/Raygun
ProductCitation IndexAI InfluenceBlogBook
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AEO Score

21

Limited Presence

Avg Prompt Score

21

across 309 prompts

AI Share of Voice

21%

across 309 prompts

Critical Issues

5

critical + high

Shortlist Position

35/80

Challenger · Error Tracking Discovery

Per-stage performance

🔍Discovery
171 category
Cited37%64/171
Share of voice19%avg
Engine consensus45%of engines
Competitors16.4avg/cited
Sentiment—no data
⚖️Evaluation
4 brand-level
Cited100%4/4
Share of voice100%avg
Engine consensus100%of engines
Competitors5.0avg/cited
Sentiment—no data
🛡️Trust
4 brand-level
Cited100%4/4
Share of voice100%avg
Engine consensus100%of engines
Competitors5.0avg/cited
Sentiment—no data
💰Conversion
4 brand-level
Cited100%4/4
Share of voice100%avg
Engine consensus100%of engines
Competitors5.0avg/cited
Sentiment—no data

Cited rate · share of voice · engine consensus · sentiment, broken out by buyer-journey stage. Sentiment is the net positive−negative skew across engines that cited the brand at this stage.

Categories Raygun is visible in

2
  • Error Tracking6 of 30→
  • Observability & APMnot yet measured→

Your visibility gap

Raygun surfaces in 35/80 Error Tracking discovery prompts. Claim this profile to see which 45 prompts you're missing and how to win them.

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Executive summary

Raygun is already cited for core discovery questions like “what is error tracking software and how does it work” and “error tracking solutions for SaaS companies,” but it’s missing evaluation-stage implementation queries (e.g., “how to implement error tracking in applications” and “error tracking for web development projects”). The single highest-ROI fix is to publish and index first-party, SSR-friendly evaluation pages that directly answer the missing gap queries (implementation + enterprise + small-team constraints) and add SoftwareApplication/FAQPage structured data to those pages.

Based on audit of raygun.com · May 6, 2026

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