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

18

Limited Presence

Avg Prompt Score

21

across 154 prompts

AI Share of Voice

21%

across 154 prompts

Category Visibility

#9

in GPU Cloud · of 48

Critical Issues

6

critical + high

Shortlist Position

12/65

Niche · Gpu Cloud Discovery

Per-stage performance

🔍Discovery
69 category
Cited23%16/69
Share of voice10%avg
Engine consensus33%of engines
Competitors18.2avg/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
Competitors4.5avg/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 Hyperstack is visible in

1
  • GPU Cloud9 of 48→

Your visibility gap

Hyperstack surfaces in 12/65 Gpu Cloud discovery prompts. Claim this profile to see which 53 prompts you're missing and how to win them.

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Executive summary

Hyperstack.cloud is already cited for GPU-cloud discovery queries (e.g., H100 availability, reserved pricing, and fine-tuning open-source LLMs), and it has strong first-party pricing transparency plus an MCP server that Claude can surface. The biggest AI-citation blocker is missing/weak coverage for high-intent evaluation patterns around specific instance types (A100/H100 + 80GB VRAM) and conversion-stage queries (spot instances, inference, real-time inference) where buyers expect crawlable, structured, instance-specific pages.

Based on audit of hyperstack.cloud · Jun 3, 2026

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