Category definition
AI Recommendation Intelligence is the discipline of measuring not just whether AI systems mention a brand, but why they recommend one brand over another, and what to do about the gap. It goes one level deeper than AI visibility: visibility asks "did the model mention us," recommendation intelligence asks "why did the model recommend a competitor instead, and what evidence would change that."
Being mentioned is not the same as being recommended. A model can name a brand while still steering the buyer toward a competitor with clearer proof, a sharper category fit, or more third-party corroboration.
AI Recommendation Intelligence treats recommendation share - the proportion of buyer-intent answers where a brand is the one recommended, not just named - as the metric that matters, and works backward from it to the specific signals driving the gap.
Where does the brand stand today, across AI Visibility, Recommendation Share, Competitive Position, Citation Strength, and Brand Accuracy. Why is a competitor being recommended instead, based on real evidence from completed provider checks. What is the highest-impact fix, ranked into a Priority Opportunities queue rather than a flat list. Did the fix work, measured by retesting the same buyer-intent prompts after remediation.
Every Trocial scan produces an AI Recommendation Profile: real extracted brand facts, real provider-by-provider evidence of how the brand is described, a competitor recommendation map with the reasons a rival may be preferred, and a prioritized action plan tied to that evidence.
This is the same loop Trocial calls Discover, Measure, Diagnose, Act, Retest - a structured cycle rather than a one-off report, so recommendation share is something a team can track and move, not a static score.
Teams in categories where buyers increasingly start research in an AI assistant instead of a search bar - B2B SaaS, fintech, cybersecurity, professional services, and agencies managing this on behalf of clients - are the clearest fit, because a single missed recommendation can mean a lost shortlist spot before a human ever visits the site.
Brand monitoring typically tracks mentions after the fact. AI Recommendation Intelligence is forward-looking: it explains the missing signals behind a low recommendation share and turns them into a fix queue, then retests.
No. Trocial measures recommendation share from real provider evidence and generates fixes grounded in that evidence. Outcomes depend on what is implemented and how AI providers respond over time; Trocial does not claim guaranteed rankings or guaranteed AI visibility.