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Examples / Seven more SEO and GEO workflows
COMMUNITY OBSERVEDπŸ€– Jev Agent

Seven more SEO and GEO workflows

Scores competitor pages, then names the element to change on each.

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Seven more SEO and GEO workflows

Overview & Result

Jev killed 7 more SEO/GEO workflows πŸ‘‡ 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 πŸ‘‡

How Jev fits in the loop

  1. Ingest real-time application state and relevant contextual parameters
  2. Format the decision problem as a bounded Choice or Noul schema
  3. Query Jev to receive a typed probability distribution in sub-50ms
  4. Execute downstream actions or route tasks according to the winning choice

How to reproduce

  1. Inspect the original showcase and source material at https://x.com/irabukht/status/2101375295152652372
  2. Verify the bounded prompt and input candidate schema configured for Jev
  3. Benchmark decision latency and classification accuracy against baseline models

Why this build matters

Demonstrates a real-world, cost-effective implementation of Jev in a search & content classification scenario, replacing expensive generative calls with fast typed decisions.

Reported performance

Reported by author

Latency: Sub-50ms deterministic decision window

Performance metrics and decision latency are reported by the original author and community benchmarks.

Limitations

  • Task accuracy is bounded by the precision of the defined candidate choices
  • Third-party external dependencies and network latency may affect total workflow duration

Patterns

Agent Evaluation