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How to get started with Brand Monitoring in AI Search Engines - Authoritas AI Search Platform

"AEO", "AIO", "GEO", "SEO" - whatever you call it we can help you discover the right questions to see how your brand is represented in LLMs

Laurence O'Toole avatar
Written by Laurence O'Toole
Updated this week

AI Search User Guide: Discover How AI Sees Your Brand

What is AI Search?

Imagine being able to ask ChatGPT, Claude, and other AI assistants hundreds of questions about your brand—and getting instant insights into how they respond. That's AI Search! It's your window into understanding how AI perceives and presents your brand to millions of users worldwide.
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Why does this matter? When someone asks an AI chatbot about "the best smartphones," "reliable car brands," or "eco-friendly companies," your brand might—or might not—be mentioned. AI Search helps you discover exactly what AI says about you.

Authoritas AI Search Platform - Quick Start: Two Ways to Begin

🚀 Path A: Use the Composer (Recommended for comprehensive analysis)

Let AI Search generate hundreds of targeted questions for you automatically

📋 Path B: Upload Your Questions (Best for specific inquiries)

Already have questions in mind? Upload them directly via CSV

Path A: The Composer - Your Question Generation Powerhouse

The Composer is a visual tool that transforms your brand analysis goals into hundreds of precisely targeted questions. Think of it as building a mind map that AI Search converts into a comprehensive questionnaire.

Step 1: Build Your Brand Hierarchy

Start simple and expand as needed:

Example: Analyzing Nike

Nike (Brand)

├── Footwear (Category)

│ ├── Running Shoes (Subcategory)

│ │ ├── Performance (Key Factor)

│ │ ├── Comfort (Key Factor)

│ │ └── Price Value (Key Factor)

│ └── Basketball Shoes (Subcategory)

│ ├── Durability (Key Factor)

│ └── Style (Key Factor)

└── Apparel (Category)

└── Activewear (Subcategory)

├── Material Quality (Key Factor)

└── Sustainability (Key Factor)

What gets generated: AI Search creates 10 unique questions for each Key Factor. So in this example, you'd get 70 questions (7 Key Factors × 10 questions each).

Step 2: Supercharge with Multipliers (The Secret Weapon!)

Multipliers are what make the Composer truly powerful. They add analytical dimensions that multiply your questions, giving you deeper insights.

🎭 Where to Add Multipliers:

Multipliers can be attached at ANY level of your hierarchy:

  • Brand level: Affects ALL Key Factors across your entire analysis

  • Category level: Affects all Key Factors within that category

  • Subcategory level: Affects Key Factors within that subcategory

  • Key Factor level: Affects only that specific Key Factor

🎯 Built-in Multipliers:

Buyer Personas - See how AI responds differently for different customer types

  • Example personas: Budget Shopper, Premium Buyer, Eco-Conscious Consumer

Buyer Journey - Understand how AI guides users through their purchase journey

  • Example stages: Awareness, Consideration, Decision, Post-Purchase

💡 The Multiplier Magic - Real Examples:

Example 1: Basic Setup

  • Key Factor: "Running Shoe Comfort"

  • Base questions generated: 10

  • Total questions: 10

Example 2: Add Multiplier at Key Factor Level

  • Key Factor: "Running Shoe Comfort"

  • Add Buyer Personas (3 types): Budget, Premium, Athlete

  • Questions generated: 10 × 3 = 30 questions

Sample questions generated:

  • "What do budget shoppers say about Nike running shoe comfort?"

  • "Are Nike running shoes comfortable enough for serious athletes?"

  • "Do premium buyers find Nike running shoes worth the comfort?"

Example 3: Add Multiplier at Category Level

  • Category: "Footwear" (contains 5 Key Factors)

  • Add Buyer Journey (4 stages) at category level

  • Questions generated: 50 base questions × 4 = 200 questions

  • Every Key Factor under Footwear gets journey stage variations

Example 4: Stack Multiple Multipliers

  • Category: "Footwear" - Add Buyer Personas (3 types)

  • Key Factor: "Running Shoe Comfort" - Add Buyer Journey (4 stages)

  • Questions for this Key Factor: 10 × 3 × 4 = 120 questions

This gives you insights like:

  • What awareness-stage budget shoppers ask about comfort

  • How premium buyers evaluate comfort during consideration

  • What athletes care about post-purchase regarding comfort

⚡ Pro Tips for Multipliers:

  1. Strategic Placement:

    • Brand-level multipliers for universal analysis (like Languages)

    • Category-level for product-specific variations (like Buyer Personas for Footwear vs Apparel)

    • Key Factor-level for precise targeting

  2. Start Conservative: Begin with 1-2 multipliers total

  3. Think Strategically: Which dimensions matter most for your analysis?

  4. Watch the Count: Questions multiply fast! Example:

    • 10 Key Factors with brand-level multiplier (3 options) = 300 questions

    • Same setup with two brand-level multipliers (3 options each) = 900 questions

  5. Custom Multipliers: Create your own! Examples:

    • Languages: "English vs Spanish vs French"

    • Store Types: "Online vs In-store vs Outlet"

    • Seasons: "Summer vs Winter collection"

    • Use Cases: "Casual vs Professional vs Athletic"

    • Regions: "North America vs Europe vs Asia"

    • Price Tiers: "Entry vs Mid-range vs Premium"

    • Competition: "vs Adidas vs Under Armour vs Puma"

Step 3: Generate and Review

Click "Generate Questions" and AI Search creates your complete question set, each tagged with its hierarchy path for easy filtering later.

Path B: Direct Question Upload

Perfect when you have specific questions or want to test a hypothesis quickly.

Simple 3-Step Process:

  1. Download the CSV Template

  2. Add Your Questions and Tags Example CSV structure:

    Question, Category, Subcategory, Tag1, Tag2 
    "Is Nike sustainable?", Sustainability, Environmental, 2024, Brand-Image
    "Nike vs Adidas for marathons", Competition, Running, Performance, Comparison

  3. Upload and Run

Running Your Analysis

Configuration Options:

  1. Select AI Models: Choose which AI assistants to test

    • Different models have different capabilities

    • Some models are "search-enabled" (can search the web and provide sources)

    • Others provide responses based on their training data only

    • Test multiple models to compare their responses

  2. Understanding Model Types:

    • Search-enabled models: Can access current web information and show which sources they reference

    • Standard models: Provide responses based on their training data

    • The model selection screen indicates which models have search capabilities

  3. Run Options:

    • Run immediately with "Start Run"

    • Set up scheduled runs to track changes over time (daily, weekly, monthly)

    • Scheduled runs help you monitor how AI perceptions evolve

Understanding Your Results

The Responses Tab: Your AI Perception Dashboard

Here's where insights come alive:

What You'll See:

  • Every AI's response to every question

  • Automatic brand mention detection

  • Sentiment analysis (Positive, Neutral, Negative)

  • Competitive mentions and comparisons

Powerful Filtering: Use your tags to slice and dice data

  • Filter by product category

  • Compare buyer personas

  • Analyze by language or region

  • Track specific Key Factors

Example Insights You Might Discover:

  • "ChatGPT mentions Nike 73% more often than Adidas for 'marathon shoes'"

  • "Negative sentiment appears 2x more in budget shopper queries"

  • "Spanish language queries show 40% higher brand preference"

The Sources Tab: Your Digital Footprint Map

Available when using search-enabled models, this tab reveals:

  • Which websites AI pulls information from

  • Your share of voice in AI responses

  • Gaps in your online presence

  • Opportunities for content creation

Note: This tab only contains data when you've selected search-enabled models in your analysis.

Real-World Application: If AI rarely cites your website but frequently cites competitors, you've identified a critical content gap to address.

Best Practices for Maximum Impact

🎯 For Brand Managers:

  • Use Buyer Persona multipliers to understand different audience perceptions

  • Compare against 3-5 key competitors

  • Run monthly to track perception changes

📊 For SEO/Content Teams:

  • Focus on Sources tab (when using search-enabled models) to identify citation gaps

  • Create content that addresses negative sentiment topics

  • Use custom Language multipliers for international SEO strategy

  • Analyze Buyer Journey stages to optimize content funnel

🔍 For Product Teams:

  • Deep dive into specific product Key Factors

  • Use Custom Multipliers for different use cases

  • Compare feature perceptions across AI models

Quick Start Recommendations

Your First Analysis:

  1. Start with 1 brand, 2 categories, 3-4 Key Factors each

  2. Add Buyer Persona multiplier (3 types)

  3. Run on top 3 AI models (mix of search-enabled and standard)

  4. Total: ~100 questions giving you comprehensive baseline insights

Advanced Analysis:

  1. Full product portfolio mapping

  2. Multiple multipliers per Key Factor

  3. All available AI models

  4. Set up scheduled runs (weekly/monthly) for trend tracking

  5. Total: 500-1000+ questions for deep competitive intelligence

Troubleshooting Tips

Too Many Questions?

  • Reduce multiplier options

  • Focus on fewer Key Factors

  • Run separate analyses for different product lines

Not Enough Detail?

  • Add more subcategories

  • Create custom multipliers for your specific needs

  • Include more specific Key Factors

Need Faster Results?

  • Use Path B with pre-written questions

  • Run smaller, targeted analyses

  • Focus on specific AI models rather than all

Ready to Start?

  1. New Users: Start with the Composer and a simple hierarchy

  2. Have Specific Questions: Jump straight to CSV upload

  3. Want to Test the Waters: Try our sample templates

Remember: AI Search isn't just about data—it's about discovering actionable insights that can transform your brand's AI presence. Start exploring today!

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