AI Visibility
measured10/100
Emerging · in AI Applications
counts brand mentions in AI answers, not source citations
AEO Readiness
fundamentals17/100
Weak fundamentals
Avg Prompt Score
0
across 137 prompts
AI Share of Voice
0%
across 137 prompts
Critical Issues
3
critical + high
How AI Visibility breaks down
in AI Applications
Composite = 60% coverage + 40% engine breadth, 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.
Categories National Institutes of Health (NIH) is visible in
1Executive summary
nih.gov is not a B2B LIMS/ELN SaaS vendor; it is a public research agency, so AI engines are unlikely to cite it as the “best LIMS/ELN software” source for buyer-evaluation prompts. The highest-ROI fix is to create crawlable, first-party evaluation content that clearly positions NIH’s ELN/LIMS guidance as a decision reference (including explicit comparisons to LabWare LIMS and Benchling) and to add structured Q&A (FAQPage) for the exact evaluation and pricing query patterns.
Based on audit of nih.gov · May 6, 2026