Methodology

How the SpotAQ score works

SpotAQ turns how AI models talk about your brand into a single score between 0 and 100. This page explains what we measure, how we calculate the score, and where its limitations are.

Methodology summary

The SpotAQ score is designed to answer one practical question: when buyers ask AI systems for tools in your category, how visible and trustworthy does your brand appear relative to competitors?

1. Inputs: what we measure

For each check, we run a controlled set of prompts against AI engines such as ChatGPT, Perplexity, and Google AI. For every response, we extract four main signals:

  • Mention rate – how often your brand is mentioned in category-level answers.
  • Sentiment – whether the answer describes you in positive, neutral, or negative terms.
  • Source quality – which domains are cited as sources: your official site vs third-party sites.
  • Google grounding – whether Google AI grounding finds your site and at which position.

2. Scoring model: how we get to 0–100

Internally, each test is scored using a simple model:

  • If your brand is not mentioned at all, that test receives a low baseline score.
  • Positive sentiment and official-site citations boost the score.
  • Negative sentiment and "we can't find information about this brand" patterns reduce the score.
  • Strong Google grounding provides an additional bonus.

The overall SpotAQ score is the average of these per-test scores across all prompts in your check.

3. Why scores can be low even when you rank in Google

Traditional SEO looks at how you rank for keywords in search results. SpotAQ looks at how AI answers questions. You can rank well in Google but still be absent from AI answers because:

  • You have few third-party reviews or directory listings.
  • Your positioning does not match the language buyers use in prompts.
  • AI has more training data and citations for larger or older competitors.

4. Data sources and privacy

We currently use a mix of public APIs and browser-based integrations to query AI engines. We do not log your prompts or brand data beyond what's required to generate your report.

  • We do not sell or resell your individual reports.
  • Aggregated, anonymized data may be used to improve the scoring model.

5. Limitations

No AI visibility metric can be perfect. Important limitations include:

  • AI models are non-deterministic; repeated runs may vary slightly over time.
  • We currently test a curated set of prompts, not every possible way a buyer might ask.
  • We do not have access to proprietary training data; we only see model outputs and cited sources.

6. How to use the score

The SpotAQ score is best used as a directional, comparative signal:

  • Tracking your own progress over time.
  • Comparing how often competitors are recommended relative to you.
  • Prioritizing work on content, Schema.org markup, and directory listings.

It should not be treated as a compliance metric or an absolute statement of truth. Instead, think of it as a structured prompt to ask better questions about how AI sees your brand.

Frequently asked questions

Why doesn't SpotAQ use search rankings alone?

Because AI answers are a different surface. Rankings help, but the key question is whether the AI answer actually mentions and recommends your brand.

Can low scores come from weak citations rather than poor product quality?

Yes. Low scores often reflect weak public evidence, fuzzy positioning, or poor official-source coverage rather than the quality of the product itself.

What usually changes the score most?

Clearer positioning, stronger comparison pages, better FAQ and schema markup, and more trustworthy third-party references tend to have the biggest impact.