Bulk Query Explorer for AI SEO

Bulk Query analysis lets you analyze multiple queries in one run instead of checking them one by one. It shows how different queries trigger LLM behavior — web search, fan-out queries, cited sources — side by side.

It’s the same analysis as our Single Query, just scaled (2-5 queries in a go) for comparison.

Because patterns only show up in comparison. When you look at multiple queries together, you can quickly see:
• which queries trigger citations
• which don’t
• which ones rely on a single source
• which ones pull from diverse sources

Bulk mode saves time and gives context.

Single Query is for depth. Bulk Query is for breadth.

Single Query helps you understand one query in detail. Bulk Query helps you compare many queries and decide which ones are worth deeper exploration.

Same engine. Different lens.

Bulk Query is useful when you’re:
• comparing topic ideas
• evaluating multiple keyword-like queries
• planning content clusters
• prioritizing which queries deserve attention

It’s especially handy for freelancers, startups, and teams working with limited time.

Each query is analyzed independently, using the same logic as Single Query. The steps that QueryCat.app follows:
• sends each query to LLM APIs
• checks if web search was triggered
• captures cited sources and metadata
• computes Citation Signal per query

There’s no shortcutting. Bulk just batches the process.

As of Dec ‘25, Bulk Query currently analyzes:
• ChatGPT - gpt-4.1-mini
• Gemini - 2.5 Flash
• Perplexity - Search tool_call that powers all their models

Each model is handled via its API (not chat UI) separately, so you can see how the same set of queries behaves across different LLMs.

For every query ran, you’ll see:
• whether web search was triggered
• fan-out queries (direct or inferred)
• cited URLs
• source diversity
• Citation Signal

Across the three LLMs. You get the results in the CSV format.

Yes. Always.

Bulk Query uses live web search behavior from LLM APIs, not cached or simulated results. Each query is processed fresh, which means results can change over time — just like LLM behavior does.

Think comparatively, not absolutely. Bulk results help you see:
• which queries tend to trigger citations
• which ones rely on memory
• which ones pull from varied sources

It’s less about “good vs bad” and more about relative behavior. And by observing those behavior you can decide which of the queries signal a citable content opportunity.

Citation Signal is a directional metric, not a prediction. It’s derived from observable LLM behavior, such as:
• how many unique sources were cited
• how diverse those sources were
• whether citations were dominated by a single source type

Here’s the exact logic used in the current version:
// Base score from source diversity
let score = Math.min(uniqueUrls.length * 15, 60);

// Bonus for multiple source categories
if (categories.size >= 3) score += 20;
else if (categories.size >= 2) score += 10;

// Penalty for single-category dominance
const maxCategoryCount = Math.max(...Object.values(categoryCounts));
if (maxCategoryCount / uniqueUrls.length > 0.7) score -= 10;

// Cap at 100
score = Math.min(score, 100);

The idea is simple: Out of multiple queries, the one that triggers more citations, from more varied sources, they are more likely to cite from multiple (and plausible different) sources another time.

No. And it’s important to say that clearly: Citation Signal is exactly what the name says—a signal, not a promise.

A few important clarifications:
• Citation Signal (CS) does not measure probability of being cited.
• A 100 CS does not guarantee a content on this topic will be cited.
• It’s meant for comparison and pattern spotting, not forecasting.

In short: it’s a signal you can observe and reason with — not a promise to rely on.

We want to be clear that our Bulk Query result does NOT:
• guarantees of citation
• promise of future behavior
• direct cause-and-effect

LLMs are probabilistic systems. QueryCat.app shows what’s happening, not what will happen. And by knowing what’s happening, you can make an informed AI SEO strategy.

"Not astrology, but astronomy of AI SEO." – This tagline of ours captures the core difference.

Most bulk AI Visibility or LLM Tracking tools try to predict outcomes — they reduce complex LLM behavior into scores, rankings, or visibility forecasts, and ask you to trust the model behind them.

QueryCat.app understands that LLMs are probabilistic and sticks to what data it can tell irrefutably – directly from the LLMs themselves. Fanout Queries, cited sources. No prediction. No recommendation. Or promise results. (You can simply take our insight and ask ChatGPT to do that.)

By keeping the mechanics visible, QueryCat.app gives you an instrument, not a prophecy — and lets you interpret the signals, compare queries in bulk, and make informed decisions without black boxes in the way.

Yes, Bulk Query is Free as of Dec ‘25.

We’re still in beta and gathering feedback. But it costs us for every query hit across each LLM, so expect a reasonable daily limit – and paid option for large queries – to be put up soon.

However, even then, the goal shall remain to keep QueryCat.app honest, useful, and affordable, not locked behind enterprise pricing.

So, use it, spread the word, and share your feedback 🙏

Firstly, you came all this way, so thanks for your interest: If you have feedback, a feature idea, or want to collaborate, you’re welcome to reach out.

Email: [email protected]
Linkedin: @mayankbishwas