The core idea

The Queraid Score answers one question: does AI know your brand exists?

To answer it, we don’t just ask one model one question. We orchestrate a systematic audit across multiple AI models with carefully designed prompts.

Step 1: Industry detection

Before we ask about your brand, we need context. We make a single call to GPT-4o-mini: “What industry does [brand] operate in?”

This seems simple, but it’s critical. Knowing the industry lets us ask relevant questions like “what are the best [industry] tools?” instead of generic queries.

Step 2: Six prompts, four models

We generate six industry-aware prompts:

Identity: “What is [brand] and what does it do?”
Best in class: “What are the best [industry] companies or tools?”
Recommendation: “I need a recommendation for [industry]. What should I use?”
Comparison: “Compare [brand] with its competitors in [industry].”
Reputation: “What do people think about [brand]?”
Use case: “Is [brand] a good choice for [industry]? Why or why not?”

Each prompt goes to all five models in parallel (GPT-4o-mini, Claude Haiku 4.5, Perplexity Sonar, Gemini 2.0 Flash, Grok 4.3). That’s 30 data points per audit.

Step 3: Analysis

For each of the 24 responses, we extract:

Mentioned? Does the response mention your brand name?
Sentiment: On a scale from -1 (very negative) to +1 (very positive), how is your brand described?
Competitors: Which other brands appear in the same response?

Sentiment analysis and competitor extraction are done via a batch call to GPT-4o-mini with structured JSON output.

Step 4: Scoring

The final score is a weighted sum of four components:

Mention Rate (40 points)

The most important factor. If AI doesn’t mention your brand, nothing else matters.

mention_score = (total_mentions / 30) × 40

A brand mentioned in all 30 responses scores a full 40. A brand mentioned in 15 out of 30 scores 20.

Sentiment (30 points)

When AI mentions your brand, is it positive? We normalize the average sentiment from [-1, +1] to [0, 1], then multiply by 30.

sentiment_score = ((avg_sentiment + 1) / 2) × 30

A perfectly positive average sentiment scores 30. Neutral scores 15. Very negative scores near 0.

Competitor Parity (20 points)

How visible are you compared to competitors? If you’re mentioned 10 times and competitors are mentioned 40 times total across all responses, that’s a 20% parity score.

competitor_score = (brand_mentions / total_competitor_mentions) × 20

Cross-LLM Consistency (10 points)

Are your mention rates consistent across all five models? Low variance means strong, uniform AI presence. High variance means some models know you but others don’t.

consistency_score = max(0, (1 - mention_variance / 0.25)) × 10

Labels

60-100: Good — Your brand has strong AI visibility
30-59: Needs Work — AI knows you, but inconsistently
0-29: Critical — Most AI models don’t mention your brand

Caching

Results are cached for 24 hours. The same brand checked twice in one day returns the same score instantly. This keeps costs predictable and results consistent within a day.

What the score doesn’t tell you

The Queraid Score measures visibility, not quality. A high score means AI models know about and mention your brand — it doesn’t mean your product is good.

Scores are also point-in-time. As AI models are retrained, responses change, and scores change with them. That’s why tracking your score monthly matters.

Want to see how your brand scores? Check it now — free, unlimited, no signup.

Want the full technical details? Read our methodology page.

How we calculate the Queraid Score