[ Use cases ] # Jev for sales teams: what it can do and what it costs How sales 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 ] - Inbound leads arrive faster than anyone can read them, and the best ones cool off while the list is sorted by hand. - Jev, TypeSafe AI's decision model, can take on these jobs for sales teams: Rank inbound leads by fit and intent before a human looks at them (1.9¢ per 1,000 leads); Sort mail into categories, rate urgency and flag what needs a reply (2.4¢ per 1,000 emails); Surface only the papers and headlines that match what you care about (2.1¢ per 1,000 papers). - 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 sales teams | | What Jev is asked | Cost per 1,000 (list price) | How good a fit | | --- | --- | --- | --- | | Rank inbound leads by fit and intent before a human looks at them | Score, Noul, Choice, 3 questions per lead | About 1.9¢ per 1,000 leads | Strong fit | | Sort mail into categories, rate urgency and flag what needs a reply | Choice, Score, Noul, 3 questions per email | About 2.4¢ per 1,000 emails | Strong fit | | Surface only the papers and headlines that match what you care about | Score, Choice, Noul, 3 questions per paper | About 2.1¢ per 1,000 papers | Strong 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 sales 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 sales teams best. Each has its own page with the questions to ask, the pitfalls and a worked example. - **Rank inbound leads by fit and intent before a human looks at them.** Lead scoring is one of Jev's strongest fits: send the lead record, score it for fit, estimate buying intent and route it, all in one request. - **Sort mail into categories, rate urgency and flag what needs a reply.** Email triage is the most widely reproduced Jev workload: one request per email can assign a category, rate urgency and flag whether a human needs to see it. - **Surface only the papers and headlines that match what you care about.** Relevance filtering is a natural Score question: describe what you care about, and Jev rates each abstract or headline against it. - [Jev for lead scoring](https://jevhub.io/use-cases/lead-scoring) - [Jev for email triage](https://jevhub.io/use-cases/email-triage) - [Jev for research and news filtering](https://jevhub.io/use-cases/research-filtering) ## 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: - **Leads (lead scoring):** about 1.9¢ per 1,000, at 3 questions and about 250 tokens each. - **Emails (email triage):** about 2.4¢ per 1,000, at 3 questions and about 400 tokens each. - **Papers (research and news filtering):** about 2.1¢ per 1,000, at 3 questions and about 350 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: - Describe a good lead in words, the way you would brief a new colleague, and turn it into a Score question. - Add a yes/no question for buying intent and a Choice for which rep or team gets the lead. - Run last quarter's leads through it and check whether the high scores are the ones that actually closed. - Use the score to order the queue first, and only automate routing once the ordering looks right. 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 sales teams A score is only as good as the description of a good lead you give it. If your closed-won history disagrees with the score, change the description, not the model. From the use-case pages, Jev is not the right choice when: - You need a written rationale for each score. Jev returns scores and probabilities, not explanations. - You want replies drafted. Jev can't generate text; pair it with a language model for the emails that need one. - You need summaries of what you keep. That's a language model's job. - [What Jev can't do](https://jevhub.io/jev-limitations) ## Our read Sales is where a cheap, fast ranking pays off most, because speed to reply matters. Treat the score as a way to read the queue in a better order, not as a verdict, and keep a human on the first conversation. 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 Yes. Lead scoring is one of Jev's strongest fits: send the lead record, score it for fit, estimate buying intent and route it, all in one request. Public runs report scoring hundreds of leads in seconds for a few cents. Yes. Email triage is the most widely reproduced Jev workload: one request per email can assign a category, rate urgency and flag whether a human needs to see it. An open-source project reports 1,000 emails triaged in about a minute for around 3 cents. Yes. Relevance filtering is a natural Score question: describe what you care about, and Jev rates each abstract or headline against it. One public run classified 1,018 research papers for about 8 cents at a 256 ms median response time. Jev lists $0.042 per million input tokens with output free. At typical volumes the calculator estimates 1.9¢ per 1,000 leads, 2.4¢ per 1,000 emails, 2.1¢ per 1,000 papers. 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. Describe a good lead in words, the way you would brief a new colleague, and turn it into a Score question. Run last quarter's leads through it and check whether the high scores are the ones that actually closed. ## 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) ## 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)