AI recommendation tracking
Track the buyer questions where AI shortlists or recommends your brand, where competitors win instead, and the evidence behind each answer.
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AI search monitoring
AI search monitoring becomes useful when every trend can be opened back to the exact buyer question, raw answer, competitor state and cited evidence.
Get a snapshotIllustrative example · not customer measurements
A closer look at the measurement.
Which tool fits a 50-person B2B SaaS team?
Surface AShortlisted3 samples
Surface BAbsent3 samples
Surface CMentioned3 samples
Surface DRecommended3 samples
What does AI search monitoring measure?
It samples answers to a defined set of prompts over time. A useful system records the engine and surface, the exact question version, the answer, citations, model, market, language, timestamp and run index.
What should stay separate?
Answer recommendations, AI referral traffic and AI crawler activity answer different questions. Recommendation Share measures the answer itself. Referrals measure downstream visits or conversions. Crawler logs diagnose whether relevant pages were fetched.
What changes after monitoring?
Open the losing questions, verify the evidence gap, ship a specific improvement, then repeat the same measurement protocol. Monitoring is the starting point; the product value is the improvement loop.
Questions people ask
Do AI monitoring tools reproduce what every user sees?
No. Results can vary by surface, model, region, language, personalization and time. A report should disclose what was actually measured.
How many runs should a baseline use?
Mentionloom targets repeated sampling—typically three to five runs per approved question per engine per cycle when budget permits.
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