Both of these are making it into the roadmap. The semantic internal linking cluster point is particularly interesting, most tools treat internal links as a PageRank signal but you’re right that LLMs parse them differently. And the specificity of claims check is something I hadn’t explicitly scored for but makes intuitive sense given how RAG systems work. Adding both.
Thanks for the detailed feedback. Those are the next items on my list now. Will add headless browser research capabilities to go around java script issues. Will also add semantic clustering check.
Seems like you are quite well versed with the space. Would you be open to sharing some interesting resources or getting on call with me to share if you have struggled with this problem and what your workflow looks like?
This is actually a broader web fetching limitation, not specific to Potatometer. Most AI crawlers like GPTBot face the same challenge with JS-rendered sites, which is itself a GEO signal worth knowing. I am exploring headless rendering to get around it. What site were you testing?
Hey HN, creator here. Happy to answer questions on how the scoring works. Potatometer checks both traditional SEO signals and GEO factors, things like structured data, citation-friendliness, entity clarity, and topical authority, then gives you specific actionable fixes rather than just a score.
Also building out AI citation scoring and a content roadmap for AI search visibility if anyone is interested in that direction.
Both of these are making it into the roadmap. The semantic internal linking cluster point is particularly interesting, most tools treat internal links as a PageRank signal but you’re right that LLMs parse them differently. And the specificity of claims check is something I hadn’t explicitly scored for but makes intuitive sense given how RAG systems work. Adding both.