How to Check What ChatGPT (and Other AI Models) Are Saying About Your Brand

Every day, millions of people ask AI assistants questions like “What’s the best project management tool for a small team?” or “Is [your brand] reliable?” The answers they receive shape purchasing decisions — often without a single click to your website.

If you’ve never checked what AI models say about your brand, you’re flying blind. This guide walks you through how to do it manually and what to look for.

Why AI brand perception matters

Search engine results used to be the first impression. Now, for a growing segment of buyers, the AI assistant’s answer is the first impression — and unlike Google results, you don’t have a meta description to influence it.

According to recent research, 72% of consumers trust AI answers about brands. The challenge: those answers are generated from training data, web crawls, and real-time retrieval — and they can be wrong, outdated, or quietly favorable to your competitors.

Step 1: Ask the four major assistants about your brand

Open each of these and treat them like a curious customer:

• ChatGPT (chat.openai.com)
• Claude (claude.ai)
• Google Gemini (gemini.google.com)
• Perplexity (perplexity.ai)

Use queries that mirror real customer intent:

• “What is [your brand]?”
• “Is [your brand] trustworthy?”
• “What are the pros and cons of [your brand]?”
• “Who are the main competitors of [your brand]?”
• “What do customers say about [your brand]?”

Run each query in a fresh session (not continuing from a previous conversation) to get an unbiased first response.

Step 2: Document what you find

For each model, note:

• Accuracy: Is the basic information (what you do, who you serve, your pricing model) correct?
• Tone: Is the sentiment positive, neutral, or negative?
• Competitive framing: Does the AI mention your competitors favorably compared to you?
• Hallucinations: Any facts that are simply invented — old prices, features you don’t offer, false reviews?

Create a simple spreadsheet: model × question, and rate each cell on accuracy and tone.

Step 3: Identify hallucinations specifically

AI hallucinations about brands are more common than most founders realize. Watch for:

• Outdated pricing: Models trained before your last price change will cite old numbers
• Discontinued products: If you’ve ever had a product you no longer offer, the AI may still recommend it
• Fabricated reviews: Some models synthesize “typical customer feedback” that never actually appeared anywhere
• Incorrect founding dates, team information, or headquarters

Any false statement should be treated as an urgent correction target.

Step 4: Check competitor comparisons

Ask each assistant: “Compare [your brand] vs [top competitor].”

Pay attention to:

• Which product is recommended for which use cases
• Whether the comparison is factually accurate
• Whether one brand consistently “wins” across the models

If a competitor is winning AI recommendations despite having an inferior product, that’s a GEO (Generative Engine Optimization) problem — not a product problem.

Step 5: Set up ongoing monitoring

Manual checks are a starting point, not a system. The problem: AI responses change as models update, as new web content gets crawled, and as competitors invest in their own AI presence.

A few options for ongoing visibility:

• Manual schedule: Repeat these checks monthly. Tedious, but free.
• Team rotation: Assign different team members to run spot checks weekly.
• Automated monitoring: Tools like BrandPing.ai run these scans continuously and alert you within minutes when something changes.

What to do when you find a problem

If you discover an inaccuracy:

1. Don’t panic: A single wrong answer doesn’t mean your brand is in crisis.
2. Identify the source: Is the wrong information on your own website, a review site, or an outdated press article? The AI likely ingested it from somewhere.
3. Correct the source: Update your website, reach out to publications with incorrect information, and respond to reviews that are being cited.
4. Build authoritative content: Create clear, factual content that establishes accurate information — the AI models do pull from current web content.
5. Monitor for improvement: After corrections, watch to see if the model’s response changes in subsequent updates.

The bigger picture

Checking what AI says about your brand is table stakes in 2026. The brands winning in AI-mediated discovery aren’t necessarily the ones with the best products — they’re the ones who know what the AI is saying and manage it proactively.

Start with a manual audit today. Then decide how you’ll keep the signal consistent over time.