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

63

Growing Presence

Avg Prompt Score

31

across 773 prompts

AI Share of Voice

30%

across 753 prompts

Category Visibility

#2

in Data Warehouse · of 29

Critical Issues

4

critical + high

Per-stage performance

🔍Discovery
504 category
Cited66%331/504
Share of voice35%avg
Engine consensus53%of engines
Competitors47.7avg/cited
Sentiment—no data
⚖️Evaluation
13 brand-level
Cited92%12/13
Share of voice92%avg
Engine consensus100%of engines
Competitors1.0avg/cited
Sentiment—no data
🛡️Trust
6 brand-level
Cited67%4/6
Share of voice67%avg
Engine consensus100%of engines
Competitors2.8avg/cited
Sentiment—no data
💰Conversion
6 brand-level
Cited100%6/6
Share of voice100%avg
Engine consensus100%of engines
Competitors1.8avg/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 Databricks is visible in

3
  • AI Infrastructure1 of 82→
  • Data Warehouse2 of 29→
  • LLM Platforms9 of 64→

Executive summary

Databricks is highly likely to be cited by AI engines for category and evaluation queries because it has strong third-party authority (e.g., 657 G2 reviews at 4.6★) and an enterprise review footprint (Gartner Peer Insights page exists). The single highest-ROI fix is to add/ensure first-party, crawlable evaluation content that directly targets the comparison and pricing prompts (especially "How does Databricks compare to Snowflake?" and "How much does Databricks cost?") with SSR + structured Q&A blocks so AI engines can cite Databricks pages instead of only third parties.

Based on audit of databricks.com · Apr 2, 2026

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