From the original post
Jev dropped the price of SEO/GEO fixes by 90%
Agents that audit and fix a client's SEO/GEO used to cost us ~$250
Here's where the savings come from:
1/ 30x faster reads of Search Console and PostHog/Mixpanel data
2/ 30x faster checks of what ChatGPT searches on Bing
3/ 30x faster modeling of what users ask Gemini and Claude
4/ 30x faster scans of who ChatGPT and Claude cite
5/ 30x faster analysis of the sources behind those citations
6/ 30x faster gap analysis: why they get cited and we don't
7/ 30x faster fixes across 1,000s of pages on large client sites
8/ 30x faster sorting of which page types ChatGPT cites
9/ 20x faster creation of the pages that make ChatGPT pick you
Available in the Ryze AI app and MCP/Claude Connector, link in the 1st comment 👇
How Jev fits in the loop
- Agent collects runtime context or input state from external sources.
- Bounded decision options are formatted into Jev System One questions.
- Jev scores each candidate option and returns a probability for it.
- Deterministic code executes the selected action or routes to specialized models.
How to reproduce
- Read the author's original post on X at https://x.com/irabukht/status/2101090579127951694.
- Inspect the described workflow architecture and bounded decision points.
- Test Jev System One decision queries against the provided task context.
Why this build matters
Demonstrates real-world Jev for SEO experimentation by @irabukht with verified community reach of 359.4k views.
Limitations
- Implementation details are based on the author's public post on X.
- Performance figures and benchmarks are author-reported community claims.
- Production deployments may require custom calibration and policy thresholds.