Albert-Weasker/niubigeo

Open-source AI brand visibility and competitor reports. Official website: https://niubigeo.ai/ | Paid services: AI testing by real people and GEO...

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Summary Information

Updated 1 hour ago
Added to GitGenius on September 21st, 2026
Created on September 3rd, 2026
Open Issues & Pull Requests: 106 (+1)
GitHub issues: Enabled
Number of forks: 686
Total Stargazers: 7,229 (+9)
Total Subscribers: 121 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 18.0 hours
Mean response time: 13.9 hours
90th percentile: 19.8 hours
Tracked items: 37

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Almost all tracked open issues have seen activity in the last three months. Only 41% of issues opened in the past year have been closed. Three people close 100% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 68
New in 7 days: 94
Closed in 7 days: 33
Avg open age: 1 days
Stale 30+ days: 6
Stale 90+ days: 0

Recent activity

Opened in 7 days: 94
Closed in 7 days: 33
Comments in 7 days: 40
Events in 7 days: 134

Top labels

  • enhancement (53)
  • bug (7)
  • question (2)

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Detailed Description

Niubigeo is an open-source tool for generating AI-powered brand visibility and competitor reports.

The tool addresses the need to monitor brand presence and competitive positioning in search results and online visibility metrics. It automates the collection and analysis of brand visibility data alongside competitor benchmarking, allowing teams to understand their market position without manual tracking across multiple sources.

The project suits organizations that need regular visibility audits and competitive intelligence but want to avoid proprietary SaaS lock-in for basic reporting. It works best for teams with technical capability to deploy and maintain the tool themselves. The repository also offers paid services for AI testing conducted by real people and GEO optimization work, providing a path to professional support for those who need it.

Development activity shows consistent engagement with the codebase. The project maintains regular updates to its core functionality and actively addresses issues raised by users. Pull requests are reviewed and merged on an ongoing basis, indicating sustained maintenance rather than dormant status. The tool is written in TypeScript, which shapes its deployment and integration patterns for teams already working in that ecosystem.