Overview & result
Ira Bodnar says Ryze AI uses Jev across seven SEO/GEO workflows: assessing competitor pages, identifying page elements to revise, checking answer coverage, estimating citation potential, sorting buyer-intent queries, proposing internal links and reviewing AI-written drafts.
From the original post
Jev killed 7 more SEO/GEO workflows 👇
1/ Assess which competitor pages to copy
-> It scores every competitor page on answer, depth, proof and freshness, then checks its rank in Google and ChatGPT to show which ones are worth copying
2/ Identify which page elements to change to get cited
-> It reads the title, meta, H1, FAQ and schema on every page and returns keep or change for each, with a confidence score
3/ Check if your pages answer what people ask AI
-> It matches real buyer questions to your best page, which shows the questions you have no page for and who AI cites instead
4/ Rate how likely each page is to get cited
-> Every URL gets a citation chance and the first fix to make, like adding a compare table
5/ Sort search terms
-> It asks "is this query from a buyer?" across the full Search Console export, so you write only for terms that convert
6/ Build the internal link map
-> For every page it checks the 15 closest candidates and links only the ones with an honest reason
7/ Verify AI-written pages
-> Each draft goes through 20 yes/no checks, and only the ones that pass reach a human
Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇
How Jev fits in the loop
- The author says competitor pages are scored for answer quality, depth, proof and freshness before deciding which ones merit attention.
- The author says page titles, metadata, headings, FAQs and schema receive keep-or-change judgments, while buyer questions and Search Console terms are matched to pages or classified for intent.
- For internal links, the post describes checking 15 nearby candidates; for AI-written drafts, it describes 20 yes/no checks before human review.
Reproduction status
The cited source does not provide public steps or code sufficient to reproduce this case.
Why this build matters
The post proposes concrete bounded decision tasks for SEO/GEO, but the public evidence supports an author-described product workflow rather than a reproducible open-source implementation.
Limitations
- The post lists product workflows but does not provide a public repository or a reproducible Jev configuration for this case.
- Citation likelihood, buyer intent and page quality are author-described product judgments, not measured search outcomes verified here.
- The associated video and post show a product demonstration; they do not establish that all seven workflows improve rankings or citations.
For inspectable implementation details, see the separate JevSEO open-source project. It is not the code behind this Ryze AI demo.