260 Interest Score
18 Discussions
0.07 Engagement
Aug 2025 Launched

Query Gold lets you find high buy intent search queries for ChatGPT that you can use for content marketing and SEO.

What the Community Said

When you're a content marketer nowadays it really helps to understand search intent and how your potential users and customers are searching. What are they asking ChatGPT, what are they searching for on Google and Youtube? If you know high buy intent search queries with real search volume you can check what kind of content already exists and start creating your own. That's why we built ⭐️ Query Gold. Query Gold lets you enter a domain of a brand (e.g. your own, a competitor, a client, …) and get

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I am already a happy findable customer, congrats on the new tool launch, will test. Seems to be another smart tool to improve our search ranking 🙌

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Ngl, the way you dig up high intent search queries (especially those long-tail gems) is crazy smart for content planning. Super helpful for getting ahead of the competition, fr!

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Happy Sunday fellow Makers. I’m the founder of QueryGold. @__tosh and I built it to help marketers understand what their customers are really searching for across Google, YouTube, and ChatGPT. It's quick, free and easy: Just enter a domain (your brand, a competitor, a client) and get instant lists of high-intent queries: Branded (“Nike running shoe prices”) Unbranded (“best shoes for flat feet”) Long-tail (“best running shoes for flat feet that also work for hiking”) It’s a super fast way to unc

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Looks like one step towards AI SEO. Definetly in the trend

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Frequently Asked Questions

A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.

Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.

Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.

The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.

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