Jev for Meta Ads: 8 jobs a decision model can take over
Jev is TypeSafe's new decision model: pick-one, score and yes/no answers for about $0.0004 each, no text. The most shared Jev demo so far is a Meta one: Matthew Berman reported breaking down 724 live ads from 37 brands in 40 seconds for 9 cents. Here are 8 Meta Ads jobs that fit, and the ones that do not. 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.
Jev on 724 live ads in 40 seconds
Matthew Berman ran Jev on 724 live ads from 37 brands. In 40 seconds it labelled every hook, format, offer, CTA and awareness stage, and flagged the landing pages that did not match the ad, for about 9 cents of tokens. This is the job most ad teams do by hand once a quarter, if at all.
What Jev looks like on a Meta Ads account: demo
One recording on a sample account. Jev pulls competitor ads, scores every one, and the winners go to an LLM to write briefs. The counters on screen are examples, not measurements.
Meta Ads: find what competitors keep running, then write ads like it
Jev reads 1,600 competitor ads, works out which styles keep running, and scores new briefs in those styles. Whole run: about $0.71.
- Pull 1,600 competitor ads from the Meta Ad Library.
- Tag each ad by type (talking head, myth-buster, price anchor) and check how long it stayed live. Ads that survive 60 days are the ones making money.
- Write and score. An LLM drafts new briefs in the winning styles; Jev scores each on hook, brand fit and how likely it is to survive.
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The 8 jobs at a glance
Meta Ads runs on volume: dozens of new creatives a week, thousands of competitor ads in the Ad Library, comments under every ad. Reading all of that with a frontier model is slow and expensive, which is why most teams sample. Tagging is a bounded question, so a decision model can read everything.
- 1Creative taggingChoice ×4
Question Jev answers: Which hook, format, offer and awareness stage does this ad use?
What happens next: Every ad gets four tags, and performance is grouped by tag
- 2Ad Library scanningNoul
Question Jev answers: Is this competitor ad new in concept or a variation of one we have tagged?
What happens next: Only new concepts reach the creative team
- 3Ad to landing page matchScore
Question Jev answers: Does the landing page deliver what this ad promises?
What happens next: Low scores are flagged before spend, not after
- 4Fatigue triageChoice
Question Jev answers: Given frequency up and CTR down for 3 days, replace, refresh or leave?
What happens next: Replace goes to the creative queue, leave is logged
- 5Comment moderationChoice
Question Jev answers: Is this comment a question, a complaint, spam or praise?
What happens next: Questions and complaints are routed, spam is hidden
- 6Policy pre-checkScore
Question Jev answers: Is this copy likely to trip a personal-attributes or health-claims policy?
What happens next: High risk is rewritten before upload
- 7Audience and creative fitChoice
Question Jev answers: Is this creative written for a cold, warm or hot audience?
What happens next: Mismatched ad sets are listed
- 8UGC brief complianceNoul ×5
Question Jev answers: Does this creator video cover the 5 points in the brief?
What happens next: Misses go back to the creator with the checklist
| # | Job | Answer type | Question Jev answers | What happens next |
|---|---|---|---|---|
| 1 | Creative tagging | Choice ×4 | Which hook, format, offer and awareness stage does this ad use? | Every ad gets four tags, and performance is grouped by tag |
| 2 | Ad Library scanning | Noul | Is this competitor ad new in concept or a variation of one we have tagged? | Only new concepts reach the creative team |
| 3 | Ad to landing page match | Score | Does the landing page deliver what this ad promises? | Low scores are flagged before spend, not after |
| 4 | Fatigue triage | Choice | Given frequency up and CTR down for 3 days, replace, refresh or leave? | Replace goes to the creative queue, leave is logged |
| 5 | Comment moderation | Choice | Is this comment a question, a complaint, spam or praise? | Questions and complaints are routed, spam is hidden |
| 6 | Policy pre-check | Score | Is this copy likely to trip a personal-attributes or health-claims policy? | High risk is rewritten before upload |
| 7 | Audience and creative fit | Choice | Is this creative written for a cold, warm or hot audience? | Mismatched ad sets are listed |
| 8 | UGC brief compliance | Noul ×5 | Does this creator video cover the 5 points in the brief? | Misses go back to the creator with the checklist |
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 meta ads means using TypeSafe's decision model for the high-volume judgment steps in meta ads. 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 creative tagging, then ad library scanning. 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. Creative tagging
Question: "Which hook, format, offer and awareness stage does this ad use?" Answer type: Choice ×4. Next: Every ad gets four tags, and performance is grouped by tag.
This is the job in Berman's demo. One state, four Choice questions, one call. Your own account and the Ad Library scrape go through the same tagger, so your winners and your competitors' are comparable.
2. Ad Library scanning
Question: "Is this competitor ad new in concept or a variation of one we have tagged?" Answer type: Noul. Next: Only new concepts reach the creative team.
A brand running 1,000 ads usually has 30 ideas. The yes/no collapses variations so the weekly scan is a short list.
3. Ad to landing page match
Question: "Does the landing page deliver what this ad promises?" Answer type: Score. Next: Low scores are flagged before spend, not after.
Berman's demo lists landing page mismatch as one of the outputs. It is also the cheapest CVR fix on most accounts.
4. Fatigue triage
Question: "Given frequency up and CTR down for 3 days, replace, refresh or leave?" Answer type: Choice. Next: Replace goes to the creative queue, leave is logged.
The numbers come from the Insights API. Jev only makes the call from a fixed set, and the 50-conversion rule stays in code so nothing is judged early.
5. Comment moderation
Question: "Is this comment a question, a complaint, spam or praise?" Answer type: Choice. Next: Questions and complaints are routed, spam is hidden.
Comments under ads affect relevance and nobody reads them at scale. Four labels and a threshold cover it.
6. Policy pre-check
Question: "Is this copy likely to trip a personal-attributes or health-claims policy?" Answer type: Score. Next: High risk is rewritten before upload.
Not a replacement for Meta's review. It catches the obvious rejections before they cost a day.
7. Audience and creative fit
Question: "Is this creative written for a cold, warm or hot audience?" Answer type: Choice. Next: Mismatched ad sets are listed.
Retargeting copy in a prospecting ad set is a common, silent waste. One label per ad exposes it.
8. UGC brief compliance
Question: "Does this creator video cover the 5 points in the brief?" Answer type: Noul ×5. Next: Misses go back to the creator with the checklist.
Works on the transcript. Five yes/no answers per video replace a manual review pass.
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A decision is only useful if something acts on it
- Ryze AI reads the live account and makes the change
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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.
Creative tagging, own account
- Rows
- 500 ads
- Approx. input tokens
- ~0.3M
- Cost at $0.042 per 1M
- ~$0.01
Ad Library scan, 10 competitors
- Rows
- 5,000 ads
- Approx. input tokens
- ~3M
- Cost at $0.042 per 1M
- ~$0.13
Comment moderation
- Rows
- 20,000 comments
- Approx. input tokens
- ~1.2M
- Cost at $0.042 per 1M
- ~$0.05
| Job | Rows | Approx. input tokens | Cost at $0.042 per 1M |
|---|---|---|---|
| Creative tagging, own account | 500 ads | ~0.3M | ~$0.01 |
| Ad Library scan, 10 competitors | 5,000 ads | ~3M | ~$0.13 |
| Comment moderation | 20,000 comments | ~1.2M | ~$0.05 |
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 hooks and scripts, for explaining why a creative won, and for the brief. Keep a human on turning ads on, budgets over 20% and anything near the pixel.
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.
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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. The Ryze AI Meta Ads MCP gives Claude, ChatGPT, Cursor and Grok live access to the account, so the collect step is one prompt.
The Ryze help center documents what the Ryze agent manages in Meta ads and approvals.
