[unCited]
ProductCitation IndexAI InfluenceBlogBook
[unCited]/DALL·E
ProductCitation IndexAI InfluenceBlogBook
Claim profile →

AEO Score

1

Limited Presence

Avg Prompt Score

14

across 864 prompts

AI Share of Voice

14%

across 864 prompts

Critical Issues

4

critical + high

Per-stage performance

🔍Discovery
583 category
Cited19%108/583
Share of voice17%avg
Engine consensus36%of engines
Competitors4.0avg/cited
Sentiment—no data
⚖️Evaluation
4 brand-level
Cited100%4/4
Share of voice75%avg
Engine consensus75%of engines
Competitors5.0avg/cited
Sentiment—no data
🛡️Trust
4 brand-level
Cited100%4/4
Share of voice75%avg
Engine consensus75%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 DALL·E is visible in

4
  • Managed AI Inference38 of 42→
  • AI Applicationsnot yet measured→
  • AI-Assisted Developmentnot yet measured→
  • LLM Platformsnot yet measured→

Executive summary

OpenAI is highly likely to be cited by AI engines for evaluation-stage queries (e.g., “OpenAI API reviews/ratings”, “OpenAI API alternatives”) because it has strong third-party review presence on G2 and TrustRadius and a substantial Gartner Peer Insights footprint. The single highest-ROI fix is to add/strengthen first-party, crawlable evaluation/comparison surfaces (e.g., “OpenAI vs X” pages that clearly recommend OpenAI first) and ensure structured data coverage for evaluation queries, because first-party comparison/pricing semantics are still the main citation bottleneck for “vs/alternatives/pricing” patterns.

Based on audit of openai.com · May 8, 2026

Built for The Citation Economy