[ Use cases ] # Jev for trust and safety teams: what it can do and what it costs How trust and safety teams can use Jev, TypeSafe AI's decision model: 3 jobs, the question for each and the list-price cost per 1,000. Checked 1 November 2026. Published 1 November 2026By Jev Hub [Back to use cases](https://jevhub.io/use-cases) [ TLDR ] - Content and messages arrive in volume, each policy is its own question, and the team needs a fast first pass before a person looks. - Jev, TypeSafe AI's decision model, can take on these jobs for trust and safety teams: Flag spam, abuse and prompt injection before content goes live (1.1¢ per 1,000 posts); Check every message and reply for jailbreaks and hazards before the LLM acts on it (2.3¢ per 1,000 messages); Tag ads by angle and hook strength, and pre-check them against policy (1.3¢ per 1,000 ads). - Each is one request per item that returns a typed value with a probability, so your own code decides what happens next. - It does not write replies, so it works alongside a person or a language model, not instead of one. ## The jobs, the questions and the cost for trust and safety teams | | What Jev is asked | Cost per 1,000 (list price) | How good a fit | | --- | --- | --- | --- | | Flag spam, abuse and prompt injection before content goes live | Noul, Score, 3 questions per post | About 1.1¢ per 1,000 posts | Good fit | | Check every message and reply for jailbreaks and hazards before the LLM acts on it | Noul, Score, 5 questions per message | About 2.3¢ per 1,000 messages | Strong fit | | Tag ads by angle and hook strength, and pre-check them against policy | Choice, Score, Noul, 3 questions per ad | About 1.3¢ per 1,000 ads | Good fit | Costs are the calculator's estimate at each use case's typical volume, at Jev's list price of $0.042 per million input tokens (read from TypeSafe's models page on 1 November 2026). They are estimates, not measured bills. ## What can trust and safety teams use Jev for? Jev, TypeSafe AI's decision model, answers a question about text with a value your software can use: one option from a list, a score, or a yes/no probability. That suits the small, repeated decisions in the middle of this work. These are the jobs on this site that fit trust and safety teams best. Each has its own page with the questions to ask, the pitfalls and a worked example. - **Flag spam, abuse and prompt injection before content goes live.** Jev works well as a fast first-pass filter: one request returns a probability for each policy you define (spam, abuse, prompt injection). - **Check every message and reply for jailbreaks and hazards before the LLM acts on it.** One Jev request per message can check it against several hazards at once, such as jailbreaks, requests for harm, medical advice and self-harm, and rate how much harm answering would do. - **Tag ads by angle and hook strength, and pre-check them against policy.** Jev can tag each ad by angle, rate hook strength and check it against your policy list in a single request, which makes it practical to label thousands of competitor or historical ads. - [Jev for content moderation](https://jevhub.io/use-cases/content-moderation) - [Jev for LLM guardrails](https://jevhub.io/use-cases/llm-guardrails) - [Jev for ad and creative classification](https://jevhub.io/use-cases/ad-classification) ## What does it cost? Jev lists $0.042 per million input tokens with output free, so the cost depends on how much text you send and how many questions you ask about it. At the typical volumes the use-case pages assume, the calculator estimates: - **Posts (content moderation):** about 1.1¢ per 1,000, at 3 questions and about 150 tokens each. - **Messages (LLM guardrails):** about 2.3¢ per 1,000, at 5 questions and about 300 tokens each. - **Ads (ad and creative classification):** about 1.3¢ per 1,000, at 3 questions and about 120 tokens each. - [Cost calculator](https://jevhub.io/calculator) - [Jev pricing and real runs](https://jevhub.io/economics) ## Where to start A plain way to begin, in order: - List your policies as separate yes/no questions, one per rule, in the words your policy uses. - Run a labelled sample of past decisions through them and note where Jev and your moderators disagree. - Set a threshold per policy, and send anything near the threshold to a person. - Keep humans on final removals and appeals, as the use-case page advises. The order to try Jev in. Our suggested order, which follows TypeSafe's advice to test on labelled data of your own before relying on the answers. - [How to use Jev](https://jevhub.io/how-to-use-jev) ## When Jev is the wrong tool for trust and safety teams Jev reads text only by default, so images, audio and video need another route, and it is a first-pass filter, not a final judge. TypeSafe's own guardrails example recommends checking both the user's message and the model's reply. From the use-case pages, Jev is not the right choice when: - You need legally defensible moderation decisions with written reasoning. - You need the reason a message was blocked written out for the user. Jev returns probabilities, not explanations. - The creative is image- or video-only with little text. - [What Jev can't do](https://jevhub.io/jev-limitations) ## Our read This is a good fit for the first pass and a poor fit for the last word. The value is that each policy gets its own probability, so your team can choose different thresholds for different harms instead of one blunt score. The figures above are list-price estimates, not measured bills, and the accuracy claims on the linked pages are the published runs' own. Test Jev on a labelled sample of your own data before you rely on it. - [What is a decision model?](https://jevhub.io/what-is-a-decision-model) ## Common questions Mostly yes. Jev works well as a fast first-pass filter: one request returns a probability for each policy you define (spam, abuse, prompt injection). Keep humans on the final call for removals and appeals. Yes. One Jev request per message can check it against several hazards at once, such as jailbreaks, requests for harm, medical advice and self-harm, and rate how much harm answering would do. Your code then passes, reviews, blocks or routes the message. TypeSafe publishes this as a cookbook and recommends checking both the user's message and the model's reply. Yes, for text. Jev can tag each ad by angle, rate hook strength and check it against your policy list in a single request, which makes it practical to label thousands of competitor or historical ads. Jev lists $0.042 per million input tokens with output free. At typical volumes the calculator estimates 1.1¢ per 1,000 posts, 2.3¢ per 1,000 messages, 1.3¢ per 1,000 ads. These are list-price estimates, not measured bills. No. According to TypeSafe, System One models such as Jev return typed answers and probabilities and do not write replies, produce code or explain their reasoning. Use a person or a language model for the writing. List your policies as separate yes/no questions, one per rule, in the words your policy uses. Set a threshold per policy, and send anything near the threshold to a person. ## Sources Written by Jev Hub from the sources below and checked against them on 1 November 2026. Where a figure is a company's own claim, the article says so. - [TypeSafe docs: System One (read 1 November 2026)](https://docs.typesafe.ai/concepts/system-one) - [TypeSafe docs: Primitives, the question types (read 1 November 2026)](https://docs.typesafe.ai/primitives) - [TypeSafe docs: models and prices (read 1 November 2026)](https://docs.typesafe.ai/models) - [TypeSafe cookbook: Twitch chat filter](https://docs.typesafe.ai/cookbooks/twitch_chat_filter) - [TypeSafe cookbook: Guardrails for LLMs](https://docs.typesafe.ai/cookbooks/llm_guardrails) ## Related content - [What is Jev?](https://jevhub.io/what-is-jev) - [How to use Jev](https://jevhub.io/how-to-use-jev) - [Jev pricing](https://jevhub.io/economics)