You would never call an LLM to compare two numbers. Yet that is roughly what we all do with AI right now: we hand a model that could draft a legal opinion a question with two possible answers, and we pay it to write a paragraph. Recently, a model arrived that is built for exactly that question. It is called Jev, and to us it is the if statement of AI.
The branch you can't write down
"Does this really need an LLM?"
Every AI feature is full of branches. Is this email a complaint or a change request? Is this inquiry for a catalog part or a custom build? Does this change request mean the drawing has to be released again? Does this invoice match the purchase order?
An if needs a condition you can write down. A comparison you can write down. "The customer sounds annoyed" you can't. So the condition moves into a prompt, and the if turns into a call to an LLM.
What that call really costs
The LLM answers in prose: "This is most likely a complaint, since the customer…". You parse it, and tomorrow the wording shifts. Or you force structured output, and the model still generates your one-word label token by token. Either way you run a generative model to produce a label. Ask it how sure it is, and it writes you a number that nobody computed.
For a yes or no, that is the most expensive if you can buy.
We do it ourselves. When a file lands in the inbox folder of our repo, an LLM reads it and writes a paragraph about where it belongs. This is the actual table it chooses from:
| What it is | Where it goes |
|---|---|
| Brand-wide asset (logo, scene, wave) | brand/assets/ |
| Study, reference, mood sample | brand/assets/referenzen/ |
| Image for one piece of content | content/<piece>/images/ |
| Legal text | legal/ |
| Research, channel or SEO material | strategy/ |
| Raw material for a story |
strategy/story-series/ if it belongs to the series, else the matching content/stories/ folder, as input for the draft |
Six destinations. A choice, dressed up as a conversation.
What Jev does differently
Jev, from TypeSafe AI, generates no text. You give it unstructured input and the set of answers you allow, and it returns one of them with a calibrated probability. The maker's own summary: "unstructured state in, typed probabilistic decisions out". An answer outside your schema can't happen, the maker says. The class name, System One model, borrows the split between fast, intuitive System 1 thinking and slow, deliberate System 2.
By the maker's numbers, Jev is about 194 times faster and 445 times cheaper than an LLM on this kind of task. Its example: 0.11 seconds against 8.6. Alex Xu of ByteByteGo puts it as 1,500 emails for about three cents.
The else belongs to a person
A probability gives the branch a real else. Above your threshold, the email goes straight to quality assurance. Below it, someone in customer service reads it first. You set the threshold, not Jev. ByteByteGo calls this a confidence gate.
That is the part we like most. An AI that tells you when it isn't sure is something we have wanted for a long time. A calibrated probability is only as good as its calibration, though. Until you have checked it against your own data, your threshold is a guess, so that check comes first.
What it can't do
Jev writes no reply to the customer and gives no reason for its choice. It can still be wrong, just within your list. And for now it is only available in early access, through a waitlist.
Our bet
Alex Xu splits the work like this: the LLM for generation, Jev for the decisions around it. We go one step further. A year from now, hardly any AI feature will let an LLM make its decisions. The LLM will only write.
Go through your prompts and count the ones that end in "answer yes or no". How many ifs are hiding in there?
This report was written in German for decision-makers at knopfdruck.ai.
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