How to Get Your Brand Discovered by ChatGPT & AI Search | CitableHub
Summary
A YouTube walkthrough from BlockVerse AI shows a project being listed on CitableHub, a free directory that formats each listing so AI assistants can read and cite it. The video covers the listing steps, a built-in guide prompt, a GEO score playbook, and what the public profile and llms.txt file expose to AI systems.
CitableHub's own pages and feeds describe structured profiles, a machine-readable llms.txt, an open API feed, and an MCP server for AI agents. The video also promotes the Arena, a live leaderboard where account holders can pay from $1 to push a project into a ranked contest.
Demonstration by 𝘉𝘭𝘰𝘤𝘬𝘝𝘦𝘳𝘴𝘦 𝘈𝘐, embedded from YouTube. AIOTruth did not reproduce the test.
Why it matters
A brand that wants to be cited by AI assistants needs its facts in a form machines can parse, and this directory offers a ready-made structured profile, feed, and agent endpoint without the brand building them. That lowers the cost of publishing machine-readable facts, but a listing only makes information available: nothing in the checked material shows that ChatGPT, Claude, Perplexity, or Gemini actually cite or recommend listed projects. The paid Arena adds a ranking that money can influence, so leaderboard position should not be read as a signal of AI visibility.
Source
- citablehub.com/ the item itself, published by 𝘉𝘭𝘰𝘤𝘬𝘝𝘦𝘳𝘴𝘦 𝘈𝘐, created by 𝘉𝘭𝘰𝘤𝘬𝘝𝘦𝘳𝘴𝘦 𝘈𝘐
- github.com/TheFortThatHolds/mail Public source code or repository
- github.com/wangshan9870/wechat-to-markdown Public source code or repository
- citablehub.com/api/llm-feed Named expert analysis, or a first-party claim about itself, first party
- citablehub.com/api/projects Named expert analysis, or a first-party claim about itself, first party
- youtube.com/watch?v=GuiftP6zs9swhere AIOTruth found it
What was checked
- canonical URL taken from the page itself
- read the item page (200)
- https://citablehub.com/api/llm-feed: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://citablehub.com/api/projects: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
- https://github.com/ai-gogoggo/music-finder: unreachable (404)
- https://github.com/TheFortThatHolds/mail: supports the item, tier 6 (Public source code or repository)
- https://github.com/wangshan9870/wechat-to-markdown: supports the item, tier 6 (Public source code or repository)
- https://citablehub.com/: supports the item, tier 8 (Named expert analysis, or a first-party claim about itself)
Limitations
- 1 linked source did not respond when checked and could not be used as evidence.
- The demonstration was not independently reproduced by AIOTruth.
AIOTruth judgment
The evidence status is Supported because CitableHub's homepage, API feed, and project feed were fetched and confirm that the directory, the structured profiles, and the machine-readable feeds exist as described. Relevance scored 10 because the item is directly about getting a brand found and cited by AI systems. Evidence scored 8 because the confirming sources are first-party claims about the product itself, and the demonstration was not independently reproduced by AIOTruth. Usefulness scored 6 and originality scored 5 because the video is a promotional walkthrough of one listing, the GEO score is the directory's own metric, and directories with llms.txt and structured data are an established pattern. One linked source did not respond when checked and could not be used as evidence.
How this score was calculated
| Dimension | Weight | Score | What produced it |
|---|---|---|---|
| AIO relevance | 30% | 10 | ai search, ai optimization, answer engine, generative engine optimization, geo; 6 scope question(s) matched |
| Usefulness | 30% | 6 | 1 artifact(s), 5 actionable marker(s), 0 measured figure(s) |
| Evidence | 25% | 8 | Public source code or repository; 5 verified source(s) across 2 domain(s) |
| Originality | 15% | 5 | no original testing found; closest archive match 0.3 |
aioRelevance x 0.30 + usefulness x 0.30 + evidence x 0.25 + originality x 0.15. The rubric is published in full on the Editorial Method page. Scoring is deterministic: the same item scores the same on every run.
Treat the claim cautiously Open the source
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