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

6

Limited Presence

Avg Prompt Score

57

across 724 prompts

AI Share of Voice

57%

across 724 prompts

Critical Issues

6

critical + high

Per-stage performance

🔍Discovery
398 category
Cited57%226/398
Share of voice52%avg
Engine consensus33%of engines
Competitors3.4avg/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 OpenSearch is visible in

3
  • Vector Databases9 of 22→
  • SIEM30 of 50→
  • Observability & APMnot yet measured→

Executive summary

OpenSearch is likely to be cited by AI engines for evaluation-stage queries because it has a strong G2 presence (4.4/5 with 15 reviews on G2) and substantial first-party technical content (e.g., RAG and vector search pages). The highest-ROI fix is to add/strengthen first-party evaluation assets—especially SoftwareApplication/FAQPage structured data and crawlable comparison/pricing-style pages—because AI engines need extractable, brand-led evidence for “OpenSearch vs X” and “OpenSearch pricing” patterns.

Based on audit of opensearch.org · May 7, 2026

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