Support ticket classification with a review path
Turn an incoming customer message into one of the queues your team already uses. Keep the category list small, the routing rule explicit, and the final action under your control.
Read the guide ↗Jev AI use cases for your next workflow.
Explore Jev AI use cases for support, sales, and product teams. Start with a bounded decision, inspect uncertainty, and connect the next workflow step.
Support and service desks
Sort incoming tickets and requests into the queues that already own the work, and keep unclear cases with a person.
An intent classification API with explicit outcomes
Route a message to the next step without asking a chat model to write a paragraph. Supply the message, your allowed intents, and the rule that separates them.
Read the guide ↗Sales and revenue
Apply your written qualification criteria to inbound leads and keep outreach under human control.
AI lead qualification using your sales criteria
A good qualification rule explains why a lead belongs in a queue. Evaluate the information a prospect actually provided against a short, written definition of fit.
Read the guide ↗AI lead scoring that reads what the lead actually wrote
Most lead scores add points for job title, company size and page views, and ignore the one field that says the most: the free text a prospect typed into your form or email. This page shows how to turn that text into a readiness band with a written rule, combine it with the score you already have, and prove it predicts revenue before sales relies on it.
Read the guide ↗Customer feedback
Turn free-text feedback into a consistent set of product themes you can count and compare.
Customer feedback tagging that your team can explain
Classify feedback by the product question you need to answer. Start with one main theme per message, then review mixed or uncertain feedback separately.
Read the guide ↗Sentiment analysis as a typed decision, not a hidden score
Sentiment is not a built-in field here. It is a question you write: a yes-or-no criterion, or an ordered rubric whose levels you define, answered for one message with a probability and a review flag.
Read the guide ↗Trust and safety
Triage text against a written policy with an explicit review outcome rather than a single automated verdict.
Content moderation classification with a review queue
Turn a written text policy into a repeatable triage step. The classifier helps sort the queue; your team remains responsible for enforcement and appeals.
Read the guide ↗OpenAI Moderation API vs custom policy classification
The OpenAI Moderation API is free, fast to add and good at one job: scoring text and images against a fixed list of harm categories. Most community and marketplace policies also contain rules that are not harm categories at all. This page explains what the endpoint returns, where its taxonomy stops, and how to layer your own policy labels on top without pretending a score is a decision.
Read the guide ↗Text classification pipelines
Feed free text into the categories your process already uses, in one request or a batch, with a review path for uncertain rows.
A text classification API that answers with your labels
Send one piece of text, the rule that separates the outcomes, and the labels your process already uses. The endpoint returns the chosen label, its probabilities when the model provides them, and a review flag.
Read the guide ↗Community showcases
Published projects built with a decision model, grouped by category, each linking to the submission it came from.
Jev showcases: what people build with a decision model
A categorised index of community submissions: agents, developer tools, interfaces, games and experiments. Each entry is one line about what it does and a link to the maker’s own post. This is an independent workspace, not the vendor.
Read the guide ↗