LLM Brand Visibility Analyzer
This tool measures how often AI-powered platforms mention, recommend, or cite your brand when users ask product-related questions. As consumers increasingly use AI assistants like ChatGPT, Perplexity, and Google's AI Overviews to research purchases, understanding your brand's visibility in these responses is crucial.
What This Tool Answers
- • When someone asks AI "What's the best [product]?", does your brand appear?
- • Which AI platforms mention your brand most frequently?
- • What sentiment surrounds your brand in AI responses?
- • How do you compare to competitors in AI recommendations?
How It Works
Enter Brand
Provide your website URL and optional brand name
Generate Queries
AI suggests realistic purchase-intent questions
Select Platforms
Choose which AI models to test against
Analyze Results
See citation rates, sentiment, and recommendations
Core Concepts
The GEO Framework
This tool is built on the Generative Engine Optimization (GEO) framework, which helps you understand not just if you're being cited, but why and wherein the customer journey.
Key GEO Principles
- • Work backwards from content — Generate queries based on what your content actually covers
- • Classify by intent — Understand what users are trying to accomplish
- • Map to funnel stages — See where you have visibility gaps in the buyer journey
- • Score content alignment — Measure how well your content matches user queries
Brand Visibility vs. Traditional SEO
Traditional SEO measures your ranking in search engine results pages (SERPs). LLM Brand Visibility measures something different: whether AI systems mention your brand in their generated responses.
Traditional SEO
- • Your website appears in search results
- • Users click through to your site
- • Based on crawled web pages
- • You control your ranking factors
LLM Visibility
- • AI mentions your brand in responses
- • Users may not click—they get the answer directly
- • Based on training data + real-time search
- • You have limited direct control
Search Platforms vs. Chat Platforms
AI platforms fall into two categories, each with different implications for your brand:
| Aspect | Search Platforms | Chat Platforms |
|---|---|---|
| Examples | Perplexity, Google AI Overviews, Gemini | ChatGPT, Claude, Meta AI |
| Data Source | Real-time web search + training data | Training data only (knowledge cutoff) |
| Citations | Provides clickable source URLs | No source links (usually) |
| Business Impact | Drives direct traffic | Builds brand awareness |
| Detection Method | Grounded (URL matching) | Text-match (keyword search) |
Detection Methods
Grounded Detection
Used for search platforms that return actual source URLs.
- • Extracts URLs from citation metadata
- • Matches your domain against cited sources
- • High confidence (actual link to your site)
- • Provides rank position in citations
Text-Match Detection
Used for chat platforms without structured citations.
- • Searches response text for brand mentions
- • Checks multiple name variations
- • Medium confidence (may miss/false-positive)
- • Extracts surrounding context
Advanced Analysis (GEO Framework)
Enable advanced features in the "Advanced Analysis" panel to unlock deeper insights into your brand visibility. All features are off by default and can be toggled on as needed.
Query Classification
User Intent
What action is the user trying to take?
Funnel Stage
Where is the user in their buying journey?
How it works: Click "Classify Queries" to automatically analyze your queries using rule-based grammar matching and ML classification.
Content Analysis
Content-Derived Queries
Instead of guessing what queries to test, derive them from your actual content. The tool analyzes your webpage and generates queries that your content should be cited for.
Match Rate Scoring
See how well each query aligns with your actual content. Low match scores indicate queries your content may not adequately address.
Enhanced Competitor Analysis
Competitor URL Extraction
For search platforms (Gemini, Perplexity), extract competitor URLs from citations. See which competitor sites are being cited instead of yours.
Competitor Benchmarking
Compare your visibility score against extracted competitors. The leaderboard shows your position relative to competitors based on citation count, mention count, and sentiment.
Coverage Analysis
When enabled, shows a visual breakdown of your query coverage across funnel stages, user intents, and content types. Identifies gaps where you have no queries or low visibility.
Gap Detection
The tool automatically identifies coverage gaps and provides recommendations:
- • Missing funnel stages: "Add educational content for awareness stage"
- • Low intent coverage: "Create comparison pages for compare intent"
- • Content type gaps: "Publish how-to guides for tutorial content"
Metrics & Scoring
Citation Rate
The percentage of AI platforms that mentioned your brand in their response.
# Formula:
Citation Rate = (Models that found brand / Total models tested) × 100
70%+
Strong visibility
30-70%
Moderate visibility
<30%
Low visibility
Sentiment Analysis
Analyzes the tone of text surrounding your brand mention using keyword detection.
Positive Indicators
recommend, best, top, excellent, quality, trusted, reliable, leading, popular, preferred
Neutral
No strong positive or negative signals detected in surrounding context
Negative Indicators
avoid, poor, worst, unreliable, expensive, disappointing, issues, problems, complaints
# Scoring Logic:
if (positiveCount > negativeCount + 1) → "positive"
else if (negativeCount > positiveCount + 1) → "negative"
else → "neutral"
Confidence Score
A 0-100% score indicating how certain the detection is. Higher confidence means stronger evidence of an intentional brand mention.
Score Components:
- +50% Base score when brand is found
- +10% For each matching identifier (URL, domain, name) up to +30%
- +10% For each additional mention (up to +30%)
- +5% For recommendation context words ("recommend", "try", "visit") up to +30%
Rank Position
For search platforms, this indicates your position in the citation list. For chat platforms, it's extracted from numbered recommendation lists.
Detection Logic
How Brand Detection Works
The tool searches for your brand using multiple name variations to catch different ways AI might reference you.
Brand Variations Generated
For URL https://www.bestbuy.com with name "Best Buy":
Detection Process
- 1Extract domain from your URL (e.g., "bestbuy" from bestbuy.com)
- 2Generate variations: spaces, hyphens, camelCase, full URL, etc.
- 3Search the AI response for any variation (case-insensitive)
- 4Extract ~100 characters of context around the mention
- 5Analyze sentiment and calculate confidence score
Competitor Detection
The tool also identifies competitors mentioned alongside your brand, helping you understand the competitive landscape in AI responses.
Known Brands Tracked
40+ brands across categories: Electronics, Fashion, Jewelry, Home, Watches
Examples: Amazon, Best Buy, Walmart, Nordstrom, Tiffany, Wayfair, IKEA, etc.
AI Platforms
The tool tests your brand against 10 AI platforms, representing the major consumer AI services people use for product research.
Search Platforms (Grounded)
Gemini 2.0 Flash
budgetGoogle AI / Search • $0.075/1M input
Gemini 2.0 Pro
premiumGemini Advanced • $1.25/1M input
Perplexity Sonar
budgetPerplexity Free • $1.00/1M input
Perplexity Sonar Pro
premiumPerplexity Pro • $3.00/1M input
Chat Platforms (Text-Match)
GPT-4o Mini
budgetChatGPT Free • $0.15/1M input
GPT-4o
premiumChatGPT Plus • $2.50/1M input
Claude 3.5 Haiku
budgetClaude Free • $0.80/1M input
Claude 3.5 Sonnet
premiumClaude Pro • $3.00/1M input
Llama 3.1 70B
budgetMeta AI • $0.52/1M input
Model Presets
Quick Check
2 models, ~$0.01/query
Gemini Flash + GPT-4o Mini
Balanced
4 models, ~$0.02/query
+ Perplexity + Claude Haiku
Comprehensive
6 models, ~$0.05/query
+ GPT-4o + Llama
Execution Modes
All Queries × All Models
Complete visibility matrix
Tests every query against every selected model. Most comprehensive but highest cost.
Tests = queries × models
All Queries × One Model
Deep dive on a single platform
Tests all queries against one chosen model. Good for platform-specific analysis.
Tests = queries × 1
One Query × All Models
Quick spot check
Tests one query across all models. Fast way to compare platforms.
Tests = 1 × models
Parallel Execution
Tests run in parallel for faster results:
- • Query-level: Up to 3 queries run simultaneously
- • Model-level: All models for a query run in parallel
- • Result: 6-10x faster than sequential execution
Budget & Costs
How Costs Are Calculated
Each API call costs based on tokens used (roughly, words processed):
Cost = (input_tokens / 1M × input_rate) + (output_tokens / 1M × output_rate)
Typical test: ~500 input tokens, ~300 output tokens per query per model
Budget Controls
Click the budget display in the header to set spending limits:
- • Default limit: $1.00 per session
- • Warning: Shown at 80% of budget
- • Presets: $0.25, $0.50, $1.00, $5.00
Mock Mode
Enable Mock Mode to test the full workflow without any API costs. Generates realistic fake results for testing and demonstration.
Interpreting Results
What Good Results Look Like
Common Issues & Solutions
Low citation rate (<30%)
Your brand may lack presence in AI training data. Focus on creating high-quality content that AI systems can reference.
Negative sentiment
AI may have learned from negative reviews or press. Address underlying issues and encourage positive reviews.
High competitors
AI often mentions multiple brands. Differentiate your value proposition clearly.
Viewing Full Results
Click any query result card to expand it and see:
- • Full AI response text with brand highlights
- • Detection details (what terms were searched)
- • Competitors mentioned in that response
- • Source citations (for search platforms)
- • Cost and latency for that specific test
Frequently Asked Questions
Why might my brand not be found even though it's well-known?
AI models have training data cutoffs and may not know recent information. They also might reference your brand differently than expected. Try adding your brand name explicitly (not just URL) and check the detection details to see what variations were searched.
What's the difference between 'found' and 'cited'?
For search platforms, being 'cited' means your actual URL appears in the sources. For chat platforms, being 'found' means your brand name was mentioned in the text. Citations drive traffic; mentions drive awareness.
How accurate is sentiment analysis?
Sentiment analysis uses keyword detection and is approximately 70-80% accurate. It may miss nuanced sentiment or sarcasm. Always review the actual response text for important decisions.
Why do different models give different results?
Each AI model has different training data, knowledge cutoffs, and response styles. This is exactly why testing across multiple platforms is valuable—it shows where your brand has visibility gaps.
What are content-derived queries?
Instead of guessing what queries to test, content-derived queries analyze your actual webpage content and generate queries that your content should be cited for. This follows the GEO framework principle of 'working backwards' from your content to understand what you should rank for.
What do the intent and funnel stage tags mean?
Intent (learn, compare, buy, etc.) describes what action the user wants to take. Funnel stage (awareness, consideration, purchase, etc.) shows where they are in the buying journey. Together, they help you understand which types of queries you're visible for and where you have gaps.
What is match rate scoring?
Match rate measures how well a query aligns with your actual content. A high match rate (60%+) means your content directly addresses that query. A low match rate (<30%) means you might not have content that answers that question, even if you want to rank for it.
How often should I run visibility tests?
Monthly testing is a good baseline. Run additional tests after major marketing campaigns, PR events, or product launches to track impact on AI visibility.
Can I improve my brand's AI visibility?
Yes! Focus on: (1) Creating high-quality, authoritative content that AI systems can learn from, (2) Getting mentioned on well-indexed sites, (3) Building a strong online presence with consistent branding, (4) Encouraging authentic positive reviews, (5) Using content-derived queries to identify gaps in your content coverage.
Need help? Check the FAQ or Core Concepts.
Launch Tool