One platform to manage how AI recommends you.
RecommendHQ is the AI Recommendation Management (ARM) platform: it unifies GEO, AEO and AI visibility into one managed loop. Measure the recommendation outcome, diagnose the citation gaps behind it, act on the highest-leverage sources, and prove the movement on the next scan.
How it works
Most tools stop at monitoring. RecommendHQ manages the whole loop. Visibility is vanity if the AI still recommends your rival.
1 · Measure
Know whether AI recommends you. Scheduled scans ask the buying-intent questions your customers ask, across ChatGPT, Perplexity, Gemini and Google AI Overviews, then score the outcome: were you recommended, cited, merely mentioned, or ghosted?
2 · Diagnose
See why the model picks a rival. Every answer is traced to the sources the model cited. The citation gap (the sources it reads for rivals but not for you) is the reason you lose the recommendation, made visible.
3 · Act
Fix what moves the score. The Action Engine converts each gap into a source-traced task, ranked by expected R-Score impact: directory claims, genuine reviews, community presence, digital PR. Effort goes where it pays.
4 · Prove
The next scan is the receipt. R-Score history over time shows the before/after delta when your work lands: proof for stakeholders, clients and exec reviews that the fix actually worked.
R-Score & Recommendation Rate
The R-Score is a normalized 0–100 measure of how likely an AI model is to recommend you for a category query, blending Presence, Prominence, Sentiment and Citation per surface, then across all four surfaces. Your Recommendation Rate is the share of AI answers that actively recommend you, not merely mention you.
Most tools report visibility: how often you appear. RecommendHQ reports the outcome, whether the AI tells buyers to choose you, and positions every market metric (AI Share of Voice, Share of Answer) against it, so one number answers the question your exec team is actually asking.
The Action Engine
Every rival the AI recommends ahead of you is quietly winning deals you never see. The Action Engine pinpoints the exact sub-forums, review sites and data sources each model reads to form those recommendations, then turns each gap into a prioritized, source-traced task, ranked by how much it will move your R-Score. No guesswork: fix what matters first, and the next scan proves it worked.
Competitive intelligence
Benchmark against the rivals AI recommends most. RecommendHQ exposes the citation footprint behind a competitor's lead: which sources the models trust, where they win the recommendation, and the shortest path to claiming it before they extend the gap. The public AI Recommendation Index shows the same scoring applied to category leaders.
Momentum: close the loop
One-off audits age instantly. Scheduled scans build R-Score history per brand, per surface, so every action taken has a measured before/after, and progress compounds into a track record you can put in front of stakeholders and clients. That is the management layer: a loop that ends in proof.
Built for agencies and in-house teams
Agencies run it white-label across the client roster. See the agency workflow. In-house SEO & content teams use it to own the AI channel and report the number upward. See the in-house workflow.
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