Launching today

NiubiGEO

Launching today

Open-source AI visibility. Human-powered growth.

65 followers

Open-source AI visibility. Human-powered growth.

65 followers

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GEO Tools

NiubiGEO combines open-source AI visibility and competitor reports with human-powered product growth. See how AI describes your product, which competitors appear, and inspect original answers and sources. Arrange paid human AI testing, product trials, content creation and distribution through the official platform. Plan tasks and budgets with Growth Canvas, then retest AI visibility. Self-hosted software is free; API, hosting and services cost extra.

Free

Launch tags:Open Source•Marketing•Growth Hacking

Launch Team / Built With

NiubiGEO

Maker

📌

Hey Product Hunt! I'm the maker of NiubiGEO. Building a product takes work. Helping people discover and understand it takes another kind of work. We built NiubiGEO to connect AI visibility research with human testing and product promotion. Here's how the pieces fit together: 1. See the evidence. Use our open-source, self-hosted tool to see how AI describes your product, which competitors appear, and what sources are returned. Compare domain-based recognition with keyword tests that don't name your brand, and inspect the original answers. 2. Check real experiences. Arrange paid tests with people using actual AI websites or apps. Agree on the questions, platforms and available regions, and receive the answers, screenshots and test conditions. You can also arrange product trials and user feedback. 3. Put a promotion plan into action. Use Growth Canvas to organize tasks, audiences and budgets. Our services cover content creation, website publishing, and community and creator distribution. Review the agreed deliverables, then run follow-up AI tests to observe what changes. The Community Edition is free under Apache-2.0. You bring your own API key and cover API and hosting costs. Human testing and promotion are optional paid services arranged through our website. API observations and human web/app tests are labeled separately. We deliver agreed work and evidence; AI platforms independently decide what they recommend. NiubiStar supports the open-source project and provides the human network behind our services. We'd love your feedback: where do you get stuck today—understanding your AI visibility, reaching relevant users, or knowing what to do next? [Explore the platform](https://niubigeo.ai/) [Explore the code](https://github.com/Albert-Weaske...)

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9h ago

Being able to see the actual AI answers and their sources is really useful. It gives mare context than just showing a visibility score.

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2h ago

I like the open source angle here. Being able to self-host the software makes it easier to understand and control how the data is handled.

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2h ago

Forum Threads

p/niubigeo

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3h ago

Title: How do you know an AI visibility change is actually real?

One problem I m curious about with GEO is the natural variation in AI answers. The same query can produce different answers, sources, or competitors at different times, so a single before-and-after test might be misleading.

For NiubiGEO, how would you recommend setting up repeated tests to separate normal AI answer variance from a genuine improvement in brand visibility?

For example, should teams run the same query multiple times across several days and compare the overall patterns rather than individual answers? And what sample size would make the change meaningful enough to act on?

p/niubigeo

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3h ago

How does NiubiGEO measure brand visibility when users don't explicitly mention the brand name?

I have been exploring generative engine optimization tools recently and find NiubiGEO quite promising. I would like to know how the platform accurately tracks brand visibility in AI responses when potential customers use general product queries without naming the specific brand. Also, what is the best workflow for a new user to set up and run their initial tests effectively?

p/niubigeo

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3h ago

How can I tell if a GEO improvement is actually working?

AI search tools can give different answers to the same query at different times. If I m tracking how often a brand appears, what would be a good way to repeat the tests and know whether a real improvement happened rather than just normal AI variation?

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