Jev is a new "System-1" AI model from TypeSafe AI that outputs decisions — labels, probabilities, and confidence scores — instead of writing text. In sales and go-to-market, that makes it a fit for classification and scoring jobs: qualifying leads, sorting inbound replies, detecting buying signals, routing, and cleaning data. It is not built to write emails. Its pitch is speed and cost: up to 200x faster and 400x cheaper than an LLM on these tasks.
What is Jev?
Jev is a foundation model from TypeSafe AI, built by Diogo Almeida, a co-creator of ChatGPT and RLHF. Instead of generating text word by word like a large language model, it outputs a decision directly: a pick from a set of options you define, with a confidence score. Because you set the possible outputs in advance, it cannot make up an answer that isn't on the list. TypeSafe reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks, with output tokens free and input metered by the billion rather than the million.
Is Jev an LLM?
No. Jev is a System-1 model: it makes fast, direct decisions. A large language model is closer to System-2: it thinks in words, one token at a time. Use an LLM when you need words written. Use Jev when you need something sorted, scored, or decided.
Jev use cases in sales and go-to-market
The best Jev use cases in GTM are the jobs where you need a label or a score, not a sentence.
| Use case | What Jev decides |
|---|---|
| Lead qualification | Is this lead in your ICP? Score each one. |
| Reply classification | Sort inbound replies: interested, not now, referral, out of office, unsubscribe. |
| Buying-signal detection | Does this job post, funding round, or LinkedIn post show intent? |
| Lead routing | Which rep, sequence, or channel should this contact go to? |
| Data hygiene | Is record A the same person as record B? Is this email valid? |
| Account prioritization | Score today's list so reps work the top first. |
| Message analysis | Sort sent messages by type to see which kind gets replies. |
Each of these runs on every lead, reply, or record you have — often thousands a day. That volume is where Jev's cost and speed matter. Scoring a million leads with an LLM is slow and expensive; with a System-1 model it is close to free.
Where Jev does not fit in GTM
Jev does not write. Anything that produces words for a human to read still needs an LLM:
- Writing or personalizing outreach messages
- Summarizing a call or an email thread
- Drafting follow-ups
A simple rule: if the output is a sentence, use an LLM. If the output is a label, a score, or a yes/no, Jev fits.
Jev vs an LLM for GTM tasks
| Jev (System-1) | LLM (System-2) | |
|---|---|---|
| Output | A decision or score | Written text |
| Best for | Sorting, scoring, routing | Writing, summarizing |
| Cost on classification | Very low | High |
| Can it invent an answer? | No, outputs are predefined | Yes |
Most GTM stacks will use both: Jev for the sorting and scoring, an LLM for the writing.
Finding what gets replies: a Jev-style job you can run today
One of the clearest GTM classification jobs is reading your own sent messages to see which kinds get replies. Pick an angle — your opening line, your ask, or the job title you wrote to — sort every message you've sent into a few types, then compare the reply rate per type. It is pure classification: no new sends, just labels on what you already have.
This is a job Sliq already does. Sliq runs LinkedIn and email outbound from Claude, ChatGPT, or any other AI agent — it finds prospects, researches them, and drafts every message, then adds each one to an approval queue for you to review. It also sorts your sent messages and prospects by type and shows the acceptance, reply, and meeting rate for each, so you can see which openers and which lead lists actually get replies. To test a change rather than read history, a Send A/B step splits a message step in two and reports the reply rate per version.
See how it works on the outreach analytics page, or read the guide on how to A/B test LinkedIn messages.
FAQ
What is Jev?
Jev is a foundation model from TypeSafe AI that outputs decisions instead of text. Rather than generating words one token at a time like a large language model, it picks from a set of options you define in advance and returns a confidence score. Because the possible outputs are fixed, it cannot invent an answer that isn't on the list. TypeSafe reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification tasks.
Is Jev an LLM?
No. Jev is a System-1 model that makes fast, direct decisions. A large language model is closer to System-2: it generates text one token at a time. Use an LLM when you need words written. Use Jev when you need something sorted, scored, or decided.
What are the best use cases for Jev in sales and GTM?
The best Jev use cases in go-to-market are jobs where you need a label or a score, not a sentence: lead qualification, sorting inbound replies, detecting buying signals, routing leads to the right rep or sequence, deduping and validating data, prioritizing accounts, and sorting sent messages by type to see what gets replies. These run on every lead, reply, or record you have, which is where Jev's low cost and speed matter.
Can Jev write sales emails?
No. Jev does not generate text, so it cannot write or personalize outreach, summarize a call, or draft follow-ups. Those still need a large language model. A simple rule: if the output is a sentence, use an LLM; if the output is a label, a score, or a yes/no, Jev fits.
How is Jev different from ChatGPT for go-to-market?
ChatGPT writes text and is a fit for drafting and personalizing messages. Jev returns a decision or score and is a fit for classifying and scoring at volume, such as qualifying leads or sorting replies. Most GTM stacks will use both: Jev for the sorting and scoring, an LLM for the writing.