Jev for GEO: 8 AI-visibility jobs a decision model can run nightly
GEO measurement has a cost problem. To know whether ChatGPT, Gemini and Perplexity recommend you, you have to read thousands of their answers, and reading with a frontier model is expensive enough that most trackers sample 40 queries. Jev, TypeSafe's decision model, reads an answer and returns yes or no for about $0.0004. Here are 8 GEO jobs it fits. Ryze AI publishes this blog and does not run Jev in production yet.
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The 8 jobs at a glance
Almost every GEO question has a bounded answer. Are we in this answer. Which competitor is. Is the mention positive. Does this page title match how the question was asked. None of that needs a model that can write, and all of it needs to run across far more queries than anyone checks today.
| # | Job | Answer type | Question Jev answers | What happens next |
|---|---|---|---|---|
| 1 | Citation checks at scale | Noul | Is our brand recommended in this answer? | Citation share per engine, per query cluster, per week |
| 2 | Who got cited instead | Choice | Which of these competitors does the answer recommend? | A share-of-answer table by competitor |
| 3 | Sentiment of the mention | Choice | Positive, neutral or negative about us? | Negative answers are flagged with the source they drew from |
| 4 | Title mirrors the question | Noul | Would someone asking this expect this page title? | No rows feed the retitle queue |
| 5 | Question coverage gaps | Choice (URL or none) | Do any of our pages answer this buyer question? | The none rows are the content calendar |
| 6 | Source triage | Choice | Is this cited source a listicle, a review site, Reddit, docs or the vendor? | An outreach list sorted by source type |
| 7 | Reddit and forum thread triage | Noul | Does this thread recommend a competitor and leave us out? | Yes threads are worth an honest answer from the team |
| 8 | Answerability score | Score | Score this page 0 to 100 on answering the question in its first 40 words | Low scores get an answer-first rewrite from the LLM |
Every row has a known answer set, which is the test for whether a decision model fits. If the answer has to be written, it belongs to an LLM.
Quick answer: Jev for geo means using TypeSafe's decision model for the high-volume judgment steps in geo. 1) It answers pick-one, score and yes/no questions for about $0.0004 each. 2) It cannot write, explain or plan. 3) The top fit here is citation checks at scale, then who got cited instead. 4) Run it behind a confidence threshold, with an approval queue for the middle band. 5) Keep a frontier model for anything a person reads and a human on anything hard to undo. 6) Access is by waitlist as of September 2026.
What Jev is, in one table
Jev is a model from TypeSafe AI, the company founded by Diogo Almeida after his work on ChatGPT at OpenAI. It opened in early access on September 15, 2026. TypeSafe calls it a System One model: you send it a block of text (the state) and a set of typed questions, and it returns typed answers with a probability attached. It does not write sentences.
| Property | What TypeSafe reports |
|---|---|
| Question types | Choice (pick one option), Score (a number in a range), Noul (a yes/no probability) |
| Price | $0.042 per 1M input tokens. Output is free. Roughly $0.0004 per decision |
| Latency | 70 to 500 ms per call, all answers returned in one parallel pass |
| Structured-output errors | 0%, because the output is schema-constrained |
| Accuracy on TypeSafe's 4-workflow benchmark | Jev 67.8%, Claude Opus 5 73.1%, GPT-5.6 Sol 74.1% |
| Access | Waitlist. Early users on X report approval the same day or the next |
| What it cannot do | Write text, code or a rationale. It gives a number and no explanation |
Two caveats belong next to those numbers. The benchmarks are vendor-reported and measure agreement with frontier models, and no large independent reproduction exists yet. And TypeSafe says itself that it cannot prove the launch price is unsubsidized. Treat the price as today's price.
The 8 jobs, one by one
1. Citation checks at scale
Question: "Is our brand recommended in this answer?" Answer type: Noul. Next: Citation share per engine, per query cluster, per week.
Collecting the answers is an API and scraping job. Judging them is the expensive part today. At this price a 40-query tracker becomes a 4,000-query tracker.
2. Who got cited instead
Question: "Which of these competitors does the answer recommend?" Answer type: Choice. Next: A share-of-answer table by competitor.
Same state, second question, same call. It is the chart a GEO report is built around.
3. Sentiment of the mention
Question: "Positive, neutral or negative about us?" Answer type: Choice. Next: Negative answers are flagged with the source they drew from.
Being cited as the expensive option is a different problem from not being cited. Track it separately.
4. Title mirrors the question
Question: "Would someone asking this expect this page title?" Answer type: Noul. Next: No rows feed the retitle queue.
AI search tends to cite pages whose titles match the question wording. This check finds the pages that answer a question under a title that hides it.
5. Question coverage gaps
Question: "Do any of our pages answer this buyer question?" Answer type: Choice (URL or none). Next: The none rows are the content calendar.
Questions come from Search Console, sales calls and the engines' own follow-ups. The match decides what to write next.
6. Source triage
Question: "Is this cited source a listicle, a review site, Reddit, docs or the vendor?" Answer type: Choice. Next: An outreach list sorted by source type.
AI answers lean on a small set of third-party pages. Knowing which type dominates your category tells you where to get mentioned.
7. Reddit and forum thread triage
Question: "Does this thread recommend a competitor and leave us out?" Answer type: Noul. Next: Yes threads are worth an honest answer from the team.
Thousands of threads, a handful that matter. The yes/no finds them.
8. Answerability score
Question: "Score this page 0 to 100 on answering the question in its first 40 words" Answer type: Score. Next: Low scores get an answer-first rewrite from the LLM.
Pages that answer directly near the top are easier for an engine to quote. The score ranks the rewrite queue.
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A decision is only useful if something acts on it
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The pattern behind every use case
Every use case on this page follows one architecture. Code finds the candidates, Jev judges them, a threshold decides what happens, and a frontier model writes only what passed.
The decide-then-act loop
- Collect the rows with code or an API: search terms, ads, pages, AI answers. No model needed.
- Shortlist with rules or embeddings, so Jev sees 10 to 15 candidates per item and not the whole account.
- Ask Jev one bounded question per row: a Choice, a Score or a Noul. Every answer comes back with a probability.
- Apply a threshold. High confidence is applied automatically. The middle band goes to an approval queue. Low confidence is dropped.
- Write with an LLM only where text is needed: the new ad, the new title, the one-line reason a client will read.
- Verify the LLM's output with Jev again before it ships.
This is the same split Ryze AI already uses between changes it makes on its own and changes that wait for approval. A cheap decision model widens the first group without touching the rule for the second.
What it costs at the list price
These are arithmetic at TypeSafe's published price of $0.042 per 1M input tokens with free output. They are not measured runs, and token counts depend on how much context you put in each state.
| Job | Rows | Approx. input tokens | Cost at $0.042 per 1M |
|---|---|---|---|
| Citation checks | 4,000 answers × 4 engines | ~10M | ~$0.42 |
| Coverage gaps | 2,000 questions | ~3M | ~$0.13 |
| Thread triage | 10,000 threads | ~6M | ~$0.25 |
The point of the table is the order of magnitude. Jobs that cost tens of dollars per run on a frontier model, and therefore run monthly, cost cents and can run nightly.
What Jev cannot do
Jev is narrow by design, and the narrow part matters as much as the price.
- No rationale. You get 0.91, never a sentence explaining it. Anything a client or a finance team will read still needs a frontier model to write the reason.
- Bounded answers only. If the set of possible answers is not known up front, it is the wrong tool. Strategy, copy and diagnosis stay with an LLM.
- Accuracy is triage-grade. 67.8% agreement on the vendor's own benchmark is fine behind a confidence threshold and wrong for auto-applying a budget change.
- "Cannot hallucinate" means it cannot break the schema. It can still pick the wrong option. Measure it against human labels on your own data before trusting a threshold.
- Waitlist and pricing risk. There is no general availability date, and the price may move.
Keep the frontier model for writing the comparison pages and answer-first rewrites, and for the narrative in a GEO report. Keep a human on which questions matter commercially and on any reply posted in a community.
Where to go deeper on each job
A decision model only sorts. The guides below cover the work around it: getting the data, writing the rules, and making the changes.
Priya N.
Brand Marketing Director
B2B Fintech
We had a monitor for a year and all it did was tell us we were losing. Ryze AI was the first thing that actually wrote and shipped the comparison pages the assistants were missing. Our citation share is 3.4x what it was, and I can point at the change log and say why.”
3.4x
AI citation share
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Assistants tracked
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To first lift
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How to start before you have access
Jev is in early access. There is no public self-serve signup yet.
1. Join the waitlist
Request access at typesafe.ai. Early users report approval the same day or within a day, but TypeSafe publishes no guarantee.
2. Label a sample first
Before you have a key, hand-label 500 rows of the job you care about. That set is how you will pick thresholds, and it works for any model.
3. Build the loop on a model you can call today
The decide-then-act loop runs on Claude Haiku 4.5 or Gemini Flash-Lite with structured outputs. It costs more per call, and the code does not change when you swap Jev in.
4. Connect the account data
The rows have to come from somewhere. Ryze AI for AI visibility gives Claude, ChatGPT, Cursor and Grok live access to the account, so the collect step is one prompt.
Sources and how this page was written
What is verified and what is not
- Verified from TypeSafe's launch post: the price, early access and the System One definition.
- Vendor-reported, not reproduced: latency, error rates and the benchmark scores.
- Third-party claims from X, not reproduced by us: the 724-ads, 586-pages and 700-leads demos.
- Our own arithmetic: every cost in the cost table, at the list price.
- Disclosure: Ryze AI publishes this blog and does not run Jev in production as of 2026-09-19.
Sources
- Introducing System One Models & Jev — TypeSafe AI, Sep 15, 2026Launch post: pricing ($0.042 per 1M input tokens, output free), early access and the System One definition.
- Jev: TypeSafe's System One Model — DataCampAPI shape, the three question types, vendor benchmark table and the stated weaknesses.
- AINews: Jev, a System One model that only decides, classifies, routes and scores — Latent SpaceIndependent summary of the launch and the open questions about the benchmarks.
- awesome-jev-by-typesafe — GitHubCommunity patterns (retrieve candidates, then judge) and the operational limits reported by early users.
Frequently asked questions
What is Jev for geo?
Jev is a decision model from TypeSafe AI, released in early access on September 15, 2026. Used for geo, it answers bounded questions about rows of account or site data: pick one option, give a score, or return a yes/no probability. It costs about $0.0004 per decision at list price and cannot write text, so it handles the sorting and an LLM handles the writing.
How much does Jev cost in 2026?
TypeSafe lists Jev at $0.042 per 1 million input tokens with output tokens free, which it describes as roughly $0.0004 per decision. That is the early access launch price from September 2026. TypeSafe itself says it cannot prove the price is unsubsidized, so budget on it as today's price and re-check before you scale a job.
How accurate is Jev compared with Claude and GPT?
On TypeSafe's own four-workflow benchmark Jev scores 67.8%, against 73.1% for Claude Opus 5 and 74.1% for GPT-5.6 Sol. The benchmark measures agreement with frontier models and nobody has independently reproduced it yet. Treat Jev as triage-grade: run it behind a confidence threshold and test it against human labels on your own data first.
Can Jev query ChatGPT or Gemini for me?
No. Jev has no tools and no web access, so it cannot send prompts to ChatGPT, Gemini or Perplexity. Another system collects the AI answers through APIs or scraping. Jev then reads each answer as text and returns a decision, for example whether your brand is recommended and which competitor is named, for a fraction of a cent.
Does Jev tell me why we were not cited?
No. Jev returns a probability with no explanation, so it can tell you that your brand is missing from an AI answer but not why. Use it to find the gaps cheaply across thousands of queries, then hand the specific answers that matter commercially to a frontier model, which can read the cited sources and explain what they have that your page lacks.
Is Jev an LLM?
TypeSafe says no. It calls Jev a System One model: a new architecture with a parallel sampler that returns every typed answer in a single pass, trained with a method named Reinforcement Learning for Calibrated Decisions. In practice the difference is simple. An LLM produces text token by token, and Jev returns a choice, a number or a probability and nothing else.
What are Choice, Score and Noul in Jev?
They are Jev's three question types. Choice picks one option from a list you provide, for example buyer, researcher or junk. Score returns a number inside a range you set, such as 0 to 100. Noul returns a yes/no probability. Every answer arrives with a calibrated confidence, which is what lets you auto-apply the confident ones and queue the rest.
How do I get access to Jev?
Jev is in early access behind a waitlist at typesafe.ai, with no general availability date as of September 2026. Early users on X report being approved the same day or within a day. You do not need to wait to start: label a sample of your data and build the same loop on Claude Haiku 4.5 or Gemini Flash-Lite, then swap the model.
Can Jev hallucinate?
Jev cannot produce malformed output, because its answers are constrained to the schema you define, and TypeSafe reports a 0% structured-output error rate. It can still be wrong. It may pick the wrong option or give a confident yes where the truth is no. That is why every workflow here uses a confidence threshold and a human approval queue for the middle band.
What can Jev not do?
Jev cannot write text, code, ad copy, page content or an explanation of its own answer. It cannot browse, call tools or read an account by itself, and it only works when the possible answers are known in advance. Strategy, diagnosis, copywriting and anything a client will read still belong to a frontier model or a person.
Does Ryze AI use Jev?
Not in production as of September 2026, because Jev is waitlist-only. Ryze AI runs the same decide-then-act loop on models that are available today: code collects the rows, a model judges them, small reversible changes are applied and larger ones wait for approval. The design lets the decision step be swapped to Jev when access opens.
What is the best way to start using Jev for geo?
Pick one high-volume job from this page, hand-label 500 rows of it, and build the loop on a model you can call today with structured outputs. Set the auto-apply threshold from your labels, not from a vendor benchmark. Connect live account data first, since a decision model is only useful when something can act on its answers.
Should a decision model replace my frontier model?
No. The two do different jobs. A decision model such as Jev takes the thousands of small, bounded calls in geo, which is where most of the token bill goes. A frontier model keeps the work that needs language: writing, rewriting, diagnosing and explaining. Most of the saving comes from routing each task to the cheapest model that can do it.
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