LLM Brand Visibility and Citation Tracker

Track how a brand appears in answers from ChatGPT, Perplexity, Gemini, and optional Claude checks. The Actor sends realistic category and customer prompts, then returns brand mentions, response rank, position, share of voice, citations, competitor co-mentions, sentiment, answer excerpts, and source URLs.

Use it for Answer Engine Optimization, Generative Engine Optimization, competitive research, citation analysis, campaign measurement, and AI agents that monitor brand discovery across LLM answer surfaces.

Best fit

• Marketing teams measuring brand presence inside AI-generated answers.
• SEO and GEO teams studying citation sources and recommendation position.
• Product marketers comparing a brand with named competitors.
• Agencies building repeatable visibility reports for clients.
• AI agents that need structured mention, rank, citation, and sentiment evidence.

A practical visibility scenario

A product marketer defines a brand, its domain, its category, and several close competitors. The Actor creates natural recommendation, alternative, how-to, and use-case prompts, sends them to the selected LLMs, and writes one row per prompt and engine. The marketer can compare is_mentioned, rank_in_response, share_of_voice_pct, and is_cited, then use all_citations to understand which sources shape the answers.

Scheduled runs turn the same prompt set into a consistent trend series for dashboards and reporting.

Quick start

The only required field is brand.

{
  "brand": "Notion"
}

A competitive audit can add the domain, category, rivals, and selected LLMs:

{
  "brand": "Notion",
  "domain": "notion.so",
  "competitors": ["Coda", "ClickUp", "Asana"],
  "category": "productivity software",
  "llms": ["chatgpt", "perplexity", "gemini"],
  "mode": "standard",
  "promptIntents": [
    "recommendation",
    "alternatives",
    "how_to",
    "use_case"
  ]
}

For a customer-language study, add custom prompts:

{
  "brand": "Notion",
  "domain": "notion.so",
  "competitors": ["Coda"],
  "customPrompts": [
    "Which workspace works well for an engineering team?",
    "How should a product team organize specifications and roadmaps?"
  ],
  "maxPrompts": 5
}

Input reference

Field	Purpose
brand	Brand, company, product, or tool being tracked.
domain	Primary website used for citation matching.
aliases	Alternate names counted as brand mentions.
competitors	Brands included in share-of-voice analysis.
category	Market category used to shape generated prompts.
llms	Selects ChatGPT, Perplexity, Gemini, and optional Claude checks.
mode	Chooses quick, standard, or deep prompt coverage.
promptIntents	Selects recommendation, alternative, comparison, how-to, use-case, review, or pricing prompts.
customPrompts	Adds exact customer or stakeholder questions.
maxPrompts	Sets a prompt cap per selected LLM.
webhookUrl	Receives the completed summary for downstream automation.
demoMode	Runs a connectivity and workflow check.

Input aliases are normalized in the runtime, which helps agents map natural field names such as company, product, rivals, engines, questions, or queries into the canonical contract.

Output data

Each dataset row represents one brand, prompt, and LLM check. Fields can include:

• llm, model, prompt, and prompt_intent
• brand, is_mentioned, and mention_count
• rank_in_response and position_score
• share_of_voice_pct and competitors_mentioned
• is_cited, brand_citation_url, and all_citations
• sentiment, excerpt, and full_answer
• run_id and checked_at

{
  "llm": "chatgpt",
  "prompt": "Which productivity workspace works well for product teams?",
  "prompt_intent": "recommendation",
  "brand": "Notion",
  "is_mentioned": true,
  "mention_count": 3,
  "rank_in_response": 2,
  "position_score": 2,
  "share_of_voice_pct": 60,
  "is_cited": true,
  "brand_citation_url": "https://notion.so/product",
  "competitors_mentioned": ["Coda"]
}

The key-value store also receives LAST_RUN_SUMMARY with the visibility index, per-LLM scores, share of voice, citation signals, and generated recommendations. A configured webhook receives the same completed summary.

Understanding the signals

Signal	Meaning
is_mentioned	The brand or one of its aliases appears in the answer.
rank_in_response	Position when the answer presents an ordered list.
position_score	Relative location of the first brand mention in the answer.
share_of_voice_pct	Brand mentions as a share of tracked competitive mentions.
is_cited	The supplied brand domain appears in answer citations or source text.
all_citations	Source URLs associated with the answer.
sentiment	Language orientation around the tracked brand.
visibility_index	Summary signal combining mention, position, and citation evidence.

AI agent workflows

The Actor is designed for Apify MCP, direct API calls, and scheduled agent workflows. Its input aliases support natural tool arguments, while the output keeps raw answers and derived metrics together.

Example agent request:

Measure LLM visibility for Notion against Coda and ClickUp in productivity software. Use recommendation and use-case prompts, return mention rank, share of voice, citations, and a summary by engine.

Useful routing guidance:

• Include domain when citation visibility matters.
• Include competitors when share of voice is part of the brief.
• Use custom prompts to mirror actual customer questions.
• Keep a stable prompt set for scheduled trend analysis.
• Read LAST_RUN_SUMMARY for compact reporting and the dataset for per-answer evidence.

Run through the API

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~llm-visibility-tracker/runs" \
  -H "Authorization: Bearer YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "brand": "Notion",
    "domain": "notion.so",
    "competitors": ["Coda", "ClickUp"],
    "category": "productivity software",
    "mode": "standard"
  }'

The Apify token stays in the Authorization header. Upstream LLM access is bundled into the Actor, so the user-facing workflow requires only Apify authentication.

How the analysis works

The runtime builds or accepts natural-language prompts, queries each selected LLM through configured provider routes, extracts native citations where available, and normalizes the answer into a shared record. Mention matching includes supplied aliases, competitor matching feeds share of voice, list parsing estimates response rank, and domain matching identifies brand citations.

The summary layer combines the per-answer signals into engine-level and overall visibility reporting while retaining each answer excerpt and citation list for review.

Pricing

This Actor uses Pay per event pricing with platform usage passed through. A visibility-check event represents one prompt and one selected LLM, and the Actor includes the upstream model access in that event. Treat the live Pricing tab as the current source of truth for event prices and billing details.

Use mode, llms, and maxPrompts to align coverage with the reporting brief.

Best results

• Provide the canonical brand domain for citation matching.
• Add common brand aliases and close competitors.
• Use customer-language prompts that represent real discovery and evaluation questions.
• Keep prompt wording stable across scheduled comparison runs.
• Review both derived metrics and the underlying excerpts and citations.

Related Actors

• Use AI Visibility Tracker when the workflow emphasizes broader brand mention and citation diagnostics across an alternate visibility contract.
• Send useful citation domains into Backlink Opportunity Finder when the next step is discovering relevant outreach and authority-building prospects.

Builder's note

I designed this Actor around an evidence-first visibility workflow. Every summary metric can be traced back to the prompt, answer excerpt, cited URLs, selected engine, and competitor set that produced it. That makes the output useful for both executive reporting and detailed agent analysis.

Responsible use

This Actor uses configured LLM provider APIs and stores results in the user's Apify dataset. Use the output for legitimate research and measurement in line with provider terms, privacy requirements, and your organization's data-governance policies.