[ Use cases ] # Jev for SEO and marketing teams: what it can do and what it costs How SEO and marketing 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 ] - Keyword lists, ad libraries and news feeds are too big to label by hand, and the labels are what make them useful. - Jev, TypeSafe AI's decision model, can take on these jobs for SEO and marketing teams: Label keyword lists by intent and relevance, at any volume (under 1¢ per 1,000 keywords); Tag ads by angle and hook strength, and pre-check them against policy (1.3¢ per 1,000 ads); 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 SEO and marketing teams | | What Jev is asked | Cost per 1,000 (list price) | How good a fit | | --- | --- | --- | --- | | Label keyword lists by intent and relevance, at any volume | Choice, Score, 2 questions per keyword | About under 1¢ per 1,000 keywords | 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 | | 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 SEO and marketing 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 SEO and marketing teams best. Each has its own page with the questions to ask, the pitfalls and a worked example. - **Label keyword lists by intent and relevance, at any volume.** Search intent is a textbook Choice question with four well-known classes: informational, commercial, transactional and navigational. - **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. - **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 SEO search-intent classification](https://jevhub.io/use-cases/search-intent-classification) - [Jev for ad and creative classification](https://jevhub.io/use-cases/ad-classification) - [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: - **Keywords (SEO search-intent classification):** about under 1¢ per 1,000, at 2 questions and about 40 tokens each. - **Ads (ad and creative classification):** about 1.3¢ per 1,000, at 3 questions and about 120 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: - Choose the label set you already use, such as the four search intents, and write it as a Choice question. - Run an export of a few thousand rows and spot-check a sample against your own judgement. - Add a Score question for how relevant each row is to your product. - Save the labels next to the data, so the next export only needs the new rows labelled. 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 SEO and marketing teams Jev labels text. It cannot see the page behind a keyword or the picture in an ad, so judge intent and ad angle from the words you give it. From the use-case pages, Jev is not the right choice when: - You need search volume or difficulty. That data comes from SEO tools, not a model. - The creative is image- or video-only with little text. - 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 Labelling at scale is the unglamorous job these models are cheapest at. The money is in the labels you can now afford to make, such as intent on every keyword, which a team would never have paid a person to do. 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. Search intent is a textbook Choice question with four well-known classes: informational, commercial, transactional and navigational. Run a keyword export through Jev and get intent plus a relevance score per keyword, for under a cent per 1,000. 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. 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 under 1¢ per 1,000 keywords, 1.3¢ per 1,000 ads, 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. Choose the label set you already use, such as the four search intents, and write it as a Choice question. Add a Score question for how relevant each row is to your product. ## 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)