AI Visibility
measured43/100
Frequently Cited · in Data & Analytics
counts brand mentions in AI answers, not source citations
AEO Readiness
fundamentals58/100
Some gaps
Avg Prompt Score
100
across 219 prompts
AI Share of Voice
100%
across 219 prompts
Critical Issues
4
critical + high
How AI Visibility breaks down
in Data & Analytics
Composite = 50% coverage + 30% engine breadth + 20% position, measured in the brand's strongest category. Position and citation depth join the score once the aggregation pipeline captures them. This is the measured Visibility score — separate from AEO Readiness, which scores fundamentals.
Per-stage performance
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.
Executive summary
scikit-learn.org is very likely to be cited by AI engines for discovery and “how to use” queries because it is a well-known, open-source ML library with strong third-party authority (e.g., G2 shows 60 reviews at 4.8/5). The biggest citation risk is evaluation/conversion-style prompts (pricing, comparisons, “is it worth it”) where scikit-learn.org has no pricing/commercial packaging and lacks AI-agent optimization signals like llms.txt/structured data.
Based on audit of scikit-learn.org · Sep 1, 2026