AEO monitoring
AEO monitoring shows which brands AI models recommend in answers
Answer Engine Optimization is about recommendation presence and framing, not just index visibility. Teams need evidence on how models rank and describe options.
What a finished report looks like
The sample report emphasizes recommendation context and wording quality for answer-engine visibility.
Carapelli
Mentions by model (demo run)
Highlight: — — the focus of this landing page. Numbers are illustrative.
Competitors in this slice
Your real report uses the same layout: scores, per-model breakdown, quotes, competitors, and citations — with your brand and the models you select.
Benchmarking
Timestamped snapshot
Completion time is stored with every run—clean before/after comparisons when you change positioning or content.
Method
Organic-style prompts
Your brand name is not pasted into the question text; we score whether models still mention you in realistic category queries.
Context
Around AEO monitoring
Add sibling models in the same check to see if the pattern is specific to AEO monitoring or repeats across the stack.
About this model
In answer-first journeys, users often decide before they click. Position and narrative context become decision-critical.
AEO monitoring reveals where to improve commercial intent pages, entity clarity, and trust signals for better recommendation outcomes.
How we measure visibility
Scenario packs run without brand names in prompt text; scoring is based on actual model answers and recommendation structure.
- Recommendation-focused snapshots for commercial intents
- Mention quality, sentiment, and adjacent competitors
- Baseline-to-rerun loops for measurable AEO progress
Inside the report
Snapshot header
Completion time and which models ran—your anchor for before/after benchmarking.
LLM-Score & share of voice
Aggregated 0–100 signal plus the share of models that mentioned your brand at least once.
Competitors & roundups
Who appears next to you in ChatGPT answers: names, frequency, comparison or recommendation context.
Quotes & wording
Answer excerpts for manual review—how the model talks about the category and your brand.
Same prompts on other models
Parallel runs (Claude, Gemini, Perplexity, …) to see if the pattern is ChatGPT-specific.
From check to PDF-ready snapshot
Brand & niche
You set brand context, site, category, language, and check type—this selects the prompt pack.
Model mix
Pick the LLM families to include; the same scenarios run in parallel across all of them.
Server run
The job executes on our side; you can close the tab and open the report from History when ready.
Report
LLM-Score, share of voice, competitors, quotes, citations—exportable and rerunnable on demand.
AEO becomes an operational channel when teams measure stable recommendation signals, not isolated anecdotes.