From the original post
Jev is INSANE for marketing.
We ran all 6 Okara agents on Jev. They completed the same work 30x faster without compromising quality
1/ Find SEO opportunities
-> It audits your site across 100+ data points, fixes technical issues, finds keyword gaps, and prioritizes changes that can grow your organic traffic
2/ Get cited by ChatGPT
-> It runs buyer prompts across ChatGPT, Claude, and Google, analyzes who gets cited and why, then creates content to close the gaps
3/ Create and publish content
-> It finds keywords your competitors rank for + related terms around your product and pulls volume and difficulty data and writes articles around the best keywords
4/ Find customers on Reddit
-> It monitors Reddit for conversations about problems your product solves, prioritizes the best threads, and writes helpful replies
5/ Grow followers and get users from Linkedin
-> It analyzes your past posts to learn what you talk about (core themes/topics) along with your voice, style, and tone, then writes new linkedin posts for you every day.
6/ Create content for X
-> It learns your topics, tone, style, and voice from past posts, then finds ideas, drafts new posts, and schedules them
Rolling out now to all Okara users
Try it at http://okara.ai
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/askOkara/status/2102319722671047139.
- 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 @askOkara with verified community reach of 31.3k 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.