Jev for SEO
SEO is full of judgments repeated across thousands of URLs, and that is exactly the shape Jev is for. 16 builds here do it: auditing and fixing client pages, finding the threads that get cited by assistants, and grading content before it ships. The figures are the ones their authors published.
Updated 22 Sept 2026 · by Made with Jev
In short
- An agency reports the same SEO and GEO audit work at roughly a tenth of its old cost, down from about $250.
- Jev judges; it does not write. Pair it with a crawl, a SERP scrape or an ad library — it brings no data of its own.
- A rubric can be 60 questions deep for about the price of one, because every question in a call is answered in parallel.
- The slop detector on this site runs on Jev: 35 tells on a page in 243 ms for $0.00015.
Why the cost per decision changes SEO work specifically
Most SEO judgments are cheap individually and ruinous in bulk. Is this page the right intent for this query. Does this competitor page actually answer the question. Is this keyword a lookalike that will pollute the set. Is this paragraph the kind of thing an assistant will quote. Each one is a rubric, and a rubric run on a large model costs somewhere between two and eighteen cents a page, which is why nobody runs it over the whole site.
At the published median of $0.000068 a decision, that same rubric over 10,000 URLs is under a dollar. The work does not get better because the model is smarter. It gets better because you can finally run it on everything instead of on a sample.
| Pass | Decisions | Cost at the median |
|---|---|---|
| One question over 1,000 URLs | 1,000 | about $0.07 |
| A 20-point rubric over 1,000 URLs | 20,000 | about $1.36 |
| A 20-point rubric over 10,000 URLs | 200,000 | about $13.60 |
Arithmetic on the median of 15 published runs, not a quote. Questions in one call are answered in parallel against the same state, so a deeper rubric costs far less than the multiplication suggests — the workings are on the pricing page.
Audit and fix pages at agency scale
The work an SEO does over and over: read a page, judge it against a rubric, name the one thing to change. Ryze AI reports the same audit and fix work at about a tenth of what it cost them before.
Ira Bodnar
@irabukht
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 👇
Ira Bodnar
@irabukht
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 👇
GitHubContent and growth
jev-seo
An agent-first SEO and GEO CLI in Rust, with DuckDuckGo and Jev.
Akash Priyadarshi
~3Stars
epergaboni
Everton Carneiro
@everton_dev
I built a tool that finds App Store keywords by reading the competition, and uses Jev to judge them. What it actually does: 1. Turns the app's own listing into a handful of search queries, with Jev filtering out the ones nobody would type. 2. Runs those searches on the App Store. Whatever ranks is the candidate pool. 3. Jev judges each candidate: is this really an alternative to the app, or does it just share a word? The lookalikes get dropped. 4. The strongest survivors become the competitor set it mines for keywords. No competitor list to maintain, nothing hallucinated: competitors are whoever Apple already ranks, minus the ones Jev rules out.
XContent and growth
App Store keywords from the real competition
Get cited by the assistants
GEO is a filtering problem before it is a writing one: which threads, which sources, which pages are worth the effort. Lurk scans 4,000 Reddit threads to find the ones an assistant is likely to quote.
Kevin Wang
@mxfp4
everyone's making demos with Jev but nobody is making real products introducing lurk.so find and monitor reddit threads to get cited by AI > FREE > 4000 reddit threads scanned > email, discord, slack only possible to give for free bc of Jev & @getanyapi
jaffa
@dsqjaffa
today i'm releasing Jev for content marketing. still doomscrolling to figure out what to post on social media? that's over now... Jev watches EVERY video in your niche and judges it before it ever reaches you: 1. research: pulls every video in your niche from a database of 12.8M viral videos 2. analyze: Jev watches, studies, and judges each one, the hooks, the formats, the angles, and why they worked 3. create: turns it into a data-backed script, based on proven winners (via Claude) no more guessing on TikTok & Instagram currently available for free in @virlomain + MCP. link below ↓
Satoshi Nagayasu 🧠🤖
@snaga
TypeSafe System One（Jev）によるHacker Newsパーソナライズ推薦の実験と複合判定アーキテクチャ gist.github.com/snaga/12c62ad5… 毎朝、Hacker NewsのチェックをAIエージェントでやってるんだけど、自分向けのレコメンドをJevで組んでみた。 なるほどー、という感じである。楽にはなりそう。
XContent and growth
Personalised Hacker News
Keep the content from reading like a machine wrote it
Every one of these asks the same shape of question — is this sentence a tell — over a whole page or a whole diff. It is the cheapest quality gate you can put in front of publishing.
Jon Kraayenbrink
@kraayenJon
jev is INSANE. in 243 ms it checked a website for 35 tells of ai slop. purple gradients. emoji headers. "seamlessly". fake testimonials. bento grids. the works. used $0.00015 of tokens. paste any url, get a slop score. free: madewithjev.com/free-tools/ai-…
Jozef
@jozef_gherman
Announcing Jev Detector The world's fastest AI slop detector, built on jev from @typesafeai ~10,000 words scanned for slop in ~2 seconds Best part, its free, no sign up required, enjoy! jevdetector.com
GitHubContent and growth
Sniff Test
A prose linter for AI writing tells: regex rules plus Jev for judgment.
Daniel Willoughby
~14
GitHubContent and growth
Clarity Judge
Writing checked on separate named axes, each with its own verdict.
TypeSafeAI (community)
4Stars
Matthew Blode
Vintuxai
@vintuxai
Stop settling for mediocre AI outputs. 🤖 Introducing slop-grader: The new Jev-AI CLI tool designed to audit text against your specific rulesets. It’s time to take control of the "slop." 🛠️ Here is the breakdown 🧵 #AITools #ProductivityAI
XContent and growth
slop-grader
Wayne Nilsen
@waynenilsen
Jev to unslop Jev as slop linter
XContent and growth
Jev as a slop linter
What we run here
This site is not a neutral observer of that last group. The AI slop detector is ours and it runs on Jev: paste a URL, it screenshots the page and asks 35 yes-or-no questions about the design and the copy in one call, and returns a slop probability. One check takes 243 milliseconds and costs $0.00015 in tokens. It is the cheapest honest demonstration of the pattern we can give you, because you can run it on your own page right now.
The other thing worth taking is the Jev Build Report: counted builds, median cost per decision, median latency and the GitHub cohort, all dated and free to cite. If you are writing about any of this, that is the page with the numbers in it.
Where it does not help
It will not write your title tags, your meta descriptions or your copy — that is a generation job and needs a writing model. It has no index, no volume data and no backlink graph, so it cannot replace the tool you pull data from; it decides about the data once you have it. And it reads text only, so a screenshot, a PDF or a video has to be turned into text first, which is what the OCR builds in the directory are doing.
Neighbouring pages: Jev for ads for the paid side, Jev for marketing for the whole function, and the use cases for every build grouped by the job rather than the role.
Common questions
- What can Jev do for SEO?
- Anything that is a judgment repeated across a lot of URLs: scoring pages against a rubric, classifying search intent, filtering a keyword set, grading content for AI tells, picking which threads or sources are worth chasing for citations. It cannot write the page or the meta description — that still needs a writing model.
- Is Jev cheaper than an LLM for a site audit?
- One agency reports the same SEO and GEO audit and fix work at about a tenth of what it cost them before, down from roughly $250. The published median across all runs in this directory is $0.000068 per decision, so a 10,000-URL rubric pass is a rounding error rather than a budget line.
- Can Jev help with GEO and AI citations?
- At the filtering end, yes. GEO work is mostly deciding which threads, sources and pages are worth the effort, and that is a classification job. Lurk scans about 4,000 Reddit threads to find the ones assistants are likely to quote.
- Does Jev detect AI-written content?
- It detects the tells, which is not the same claim. Several builds here ask a fixed list of yes-or-no questions about a page or a diff — em-dash patterns, hedging, bento grids, fake testimonials — and return a probability per tell. The slop detector on this site checks 35 of them in 243 ms for $0.00015.
- Can Jev replace my SEO tool?
- No. It has no index, no crawler and no keyword database. It is the judgment layer you put on top of the data you already pull, which is why the builds here pair it with DuckDuckGo, the Meta Ad Library, Reddit or a crawl you ran yourself.
Made with Jev is independent and not affiliated with TypeSafe AI. Every figure on this page is the one its author published, linked to where it can be checked.