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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What is Jev: a 45-second explainer for marketers
ChatGPT answers a question by writing a paragraph. Jev answers by picking from a list. Ask both "is this invoice fraud?" ChatGPT types out a sentence in 8 seconds. Jev gets three options, Fraud / Clean / Review, and returns a score for each in 0.1 seconds: Clean, 88%. No writing, no explaining, just the pick. That is Jev: a model that only decides, 100x faster and far cheaper. Every job on this page is a pick from a list.
What Jev looks like on AI search: demo
One recording on a sample account. Jev reads thousands of AI answers and tells you where you are cited, who is cited instead, and what to fix. The counters on screen are examples, not measurements.
GEO: check if ChatGPT, Claude and Gemini recommend you, and fix the pages that lose
Jev checks 1,450 buyer questions across three AI engines and tells you where you are cited, who is cited instead, and which pages to fix first.
- Ask ChatGPT, Claude and Gemini about 1,450 questions your buyers ask.
- Check every answer for two things: are you mentioned, and if not, who is.
- Fix. For the pages that lose, Jev scores why (too thin, wrong angle, no data) and an LLM turns that into a rewrite checklist, in order.
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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.
- 1Citation checks at scaleNoul
Question Jev answers: Is our brand recommended in this answer?
What happens next: Citation share per engine, per query cluster, per week
- 2Who got cited insteadChoice
Question Jev answers: Which of these competitors does the answer recommend?
What happens next: A share-of-answer table by competitor
- 3Sentiment of the mentionChoice
Question Jev answers: Positive, neutral or negative about us?
What happens next: Negative answers are flagged with the source they drew from
- 4Title mirrors the questionNoul
Question Jev answers: Would someone asking this expect this page title?
What happens next: No rows feed the retitle queue
- 5Question coverage gapsChoice (URL or none)
Question Jev answers: Do any of our pages answer this buyer question?
What happens next: The none rows are the content calendar
- 6Source triageChoice
Question Jev answers: Is this cited source a listicle, a review site, Reddit, docs or the vendor?
What happens next: An outreach list sorted by source type
- 7Reddit and forum thread triageNoul
Question Jev answers: Does this thread recommend a competitor and leave us out?
What happens next: Yes threads are worth an honest answer from the team
- 8Answerability scoreScore
Question Jev answers: Score this page 0 to 100 on answering the question in its first 40 words
What happens next: Low scores get an answer-first rewrite from the LLM
| # | 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.
Question types
- What TypeSafe reports
- Choice (pick one option), Score (a number in a range), Noul (a yes/no probability)
Price
- What TypeSafe reports
- $0.042 per 1M input tokens. Output is free. Roughly $0.0004 per decision
Latency
- What TypeSafe reports
- 70 to 500 ms per call, all answers returned in one parallel pass
Structured-output errors
- What TypeSafe reports
- 0%, because the output is schema-constrained
Accuracy on TypeSafe's 4-workflow benchmark
- What TypeSafe reports
- Jev 67.8%, Claude Opus 5 73.1%, GPT-5.6 Sol 74.1%
Access
- What TypeSafe reports
- Waitlist. Early users on X report approval the same day or the next
What it cannot do
- What TypeSafe reports
- Write text, code or a rationale. It gives a number and no explanation
| 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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Ryze AI — Autonomous Marketing
A decision is only useful if something acts on it
- Ryze AI reads the live account and makes the change
- Approval queue for budgets, bids and money pages
- Free to connect, flat fee for autopilot
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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.
Citation checks
- Rows
- 4,000 answers × 4 engines
- Approx. input tokens
- ~10M
- Cost at $0.042 per 1M
- ~$0.42
Coverage gaps
- Rows
- 2,000 questions
- Approx. input tokens
- ~3M
- Cost at $0.042 per 1M
- ~$0.13
Thread triage
- Rows
- 10,000 threads
- Approx. input tokens
- ~6M
- Cost at $0.042 per 1M
- ~$0.25
| 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
8
Assistants tracked
11 weeks
To first lift
1,000+ marketers use Ryze
Automating hundreds of agencies
★★★★★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.
If you want the documentation behind this, the Ryze help center has pages on what generative engine optimization (GEO) is and how Ryze tracks your brand in ChatGPT, Gemini and Perplexity.
