FREE ONLINE AI TEXT CLASSIFIER
JEV AI Model: free online AI text classifier
Classify text or JSON into probabilities, scores, and labels with confidence. Start free in your browser, with no API key required.
3 free runs without an account. Sign in with Google for 30 runs per day (UTC). No API key needed.
Checking allowance...
Structured answers
Get a probability, score, or one of your labels instead of free-form text.
Confidence included
Noul returns a probability. Score and Choice also include confidence to help identify cases for review.
No setup
Try three classifications without an account or API key, then sign in to continue.
What is Jev AI, and what can you do here?
Jev is a TypeSafe AI model for structured decisions: it evaluates the context and questions you provide, then returns a category, score, or probability. JEV AI Model at jevaimodel.app is a free browser playground that sends your classification requests to the TypeSafe API. Try it with support messages, survey answers, product listings, feedback, or other text and JSON records that need a consistent answer format.
A classifier differs from a general chatbot because you choose the shape of the answer before you run it. Instead of requesting a paragraph and then extracting its meaning, you ask for a probability, an ordered rating, or one label from a controlled list. The result is easier to store, compare, filter, audit, and pass to another step in a workflow.
That structure does not remove uncertainty. It makes uncertainty visible. JEV AI Model returns the relevant probabilities and, where the answer type supports it, a confidence value. You can automate clear cases, review ambiguous ones, and revise the context or label definitions when results are not distinct enough. Treat every classification as model output that should be verified before a consequential decision.
How to use Jev online
Start with “Try a free example” or choose “Triage a support message” to load a complete example. You can inspect and edit its context and questions before running it. Follow these three steps for your own text or JSON.
- 1Load an example or add contextChoose an example to fill the context and questions, or paste your own text and describe the relevant facts as JSON. Read the input before sending it and include the evidence needed for the decision. Stable field names make it easier to reuse a question across records. Only submit information you are authorized to process.
- 2Choose an answer typeSelect Noul when the answer is a likelihood, Score when it belongs on an ordered scale, or Choice when it must be one of several named labels. Write one focused instruction and define levels or options so they are specific, distinct, and collectively useful. Clear boundaries produce results that are easier to interpret than vague or overlapping categories.
- 3Run and review the classificationSelect Run to send your request. Noul returns a probability; Score and Choice also show confidence and the probabilities of the available answers. For a Choice result, compare the selected label with the alternatives. A narrow margin can signal an ambiguous case. Review the context and label definitions, make a specific change, and run again to see how it affects the answer.
Three AI classification output types
Noul - probability
Use Noul for a statement that can be more or less likely to be true. It returns a number from 0 to 1: values near 1 support the statement, values near 0 oppose it, and values near 0.5 show meaningful uncertainty. Examples include whether a support message is urgent, a review expresses satisfaction, or a prompt attempts to bypass a policy.
Score - ordered rating
Use Score when possible answers have a natural order. Define at least two levels such as no impact, minor, major, and blocking. The result can fall between levels, so a value such as 1.4 preserves more information than forcing the record into level 1 or 2. The probability distribution also shows which neighboring levels competed for the answer.
Choice - category label
Use Choice when the answer must come from a set of named options without an inherent order. Give every option a short description of what it includes and, when useful, what it excludes. The classifier returns the selected label, confidence, and probability assigned to each option. That full distribution helps reveal overlapping labels and borderline cases that need review.
Popular AI text classification use cases
A free online AI classifier is most useful when many records need the same well-defined judgment. Start with a small representative sample, inspect uncertain outputs, and refine the question before applying the workflow more broadly.
Support ticket classification
Classify issue type, detect urgency, estimate sentiment, or route a message to billing, account, and technical teams. Include the customer message and the routing definitions in the context. Review tickets with similar top probabilities instead of automatically sending a genuinely ambiguous case to the wrong queue.
Résumé screening
Compare evidence in a résumé with explicit job requirements and rate relevance on a defined scale. Keep the criteria job-related, test them for inconsistent outcomes, and send uncertain applications to a person. AI classification should support a documented review process, not make an unreviewed employment decision.
Content moderation
Map posts, listings, comments, or prompts to the policy categories you define. Add the relevant policy text to the context and keep enforcement separate from classification, especially when nuance matters. The probability distribution can identify edge cases for human review and show when two policy labels overlap.
Survey coding and data labeling
Turn open-text responses, feedback, and raw records into consistent themes for analysis. Define the taxonomy before classifying, include an other or unclear option when appropriate, and audit a sample of every label. Reusable questions make it easier to compare results across batches without rewriting a prompt for each record.
AI text classifier FAQ
Is JEV AI Model free to use online?
Yes. This playground includes three free runs in total without an account. Sign in with Google for up to 30 runs per day, resetting at 00:00 UTC. You do not need to provide an API key. These allowances apply to the playground on this website; an API integration has its own provider access and billing terms.
Can I use Jev online without installing anything?
Yes. Open this playground in your browser, choose an example or enter your own context, and select Run. You need an internet connection because requests are processed through the TypeSafe API. The page shows how many playground runs you have left.
What is the difference between Jev and this playground?
Jev is the TypeSafe AI model that evaluates your questions. JEV AI Model is the web interface on jevaimodel.app for trying those decisions with text or JSON. Use Noul for a probability, Score for an ordered rating, or Choice for a label from your own options. The API integration guide in the navigation covers using Jev in an application.
Can I classify JSON as well as plain text?
Yes. Paste normal text or provide a JSON object containing the facts the model should evaluate. JSON is useful when records share fields such as message, category, account tier, or policy. Keep the structure consistent across records and include human-readable values; field names alone should not have to carry the meaning of the task.
What can the AI text classifier return?
It can return the probability that a statement is true, a numeric score across ordered levels, or one category from options you define. Score and Choice results also expose their probability distribution, which helps you compare the selected answer with nearby alternatives rather than treating every winning label as equally certain.
Do I need an account or API key?
You can start with three guest runs without an account or your own API key. To continue after using them, sign in with Google for a daily allowance of 30 runs. If you reach that daily limit, wait until the allowance resets at 00:00 UTC. The remaining allowance is shown beside the Run button.
How should I evaluate confidence and accuracy?
Test the classifier on representative examples for which you already know the desired outcome. Inspect incorrect results, close probability distributions, and differences across important subgroups. Set a review threshold based on the cost of an error, not a universal number. Low-confidence results should usually go to a person, and consequential decisions should always receive appropriate independent review.