Grok · LLM monitoring
X-native narratives—why Grok matters for your brand
Your buyers live in the X timeline—crypto, markets, breaking news. Grok ships inside the X ecosystem, so it inherits that conversational context. A Grok slice shows whether your social narrative carries into AI answers or vanishes.
What a finished report looks like
The demo highlights Grok’s row: mention rate and quotes pulled specifically from Grok answers beside neutral chat models.
Carapelli
Mentions by model (demo run)
Highlight: Grok — 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 Grok
Add sibling models in the same check to see if the pattern is specific to Grok or repeats across the stack.
About this model
xAI positions Grok alongside X, so heavy overlap with live threads, finance chatter, and media velocity is the default audience.
Training signals lean on the firehose of X posts—brands active on Twitter/X inform the model informally, yet few teams monitor that path separately from classic SEO.
How we measure visibility
Organic scenarios without the brand in the question; score Grok’s answer and compare with peers in the same run.
- Grok next to ChatGPT, Claude, Gemini, and more
- Sentiment, competitors, quotes
- Repeatable snapshots for comms teams
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 Grok 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 Grok-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.
Large Grok vs others gaps should trigger a source and social-narrative review.