AI Visibility
measured71/100
Frequently Cited · in Vector Databases
counts brand mentions in AI answers, not source citations
AEO Readiness
fundamentals63/100
Some gaps
Avg Prompt Score
63
across 682 prompts
AI Share of Voice
63%
across 666 prompts
Critical Issues
2
critical + high
How AI Visibility breaks down
in Vector Databases
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 Mongodb is visible in
2Executive summary
MongoDB is highly likely to be cited by AI engines for evaluation and trust queries because it has very strong third-party review presence (e.g., G2 seller page shows 899 reviews for MongoDB Atlas) and first-party comparison content (e.g., /resources/compare pages). The single highest-ROI fix is to address Technical AEO risk: robots.txt appears to disallow /llms.txt on mongodb.com, which can reduce AI-agent discoverability of the most relevant documentation index.
Based on audit of mongodb.com · Aug 16, 2026