Use cases

Where you can use Jev

Jev fits anywhere a person reads something and makes a quick call about it: which team, how urgent, is it allowed. Here are 20 common places teams use it, and the questions Jev answers in each.

A good fit when…

  • A person could make the call in a second or two after reading the input.
  • The answer is a category, a level on a scale, or a yes/no.
  • You make the same kind of decision many times a day, at volume.
  • You want a probability or confidence, not just a label, so you can set thresholds.

Not what Jev is for

  • Writing or summarising text (use a regular LLM for that).
  • Multi-step reasoning, maths or calculations.
  • Extracting exact values like dates or amounts into fields.
  • Judging images, audio or video (Jev reads text today).

Customer support

Support ticket triage

Route each ticket to the right team, rate urgency and spot refund requests.

Example scenario: We receive customer support emails. Route each one to billing, technical support or sales, rate how urgent it is, detect whether the customer is asking for a refund, and flag emails a human must review before any automated reply.

Typical users: Support and CX teams, helpdesk tools

Questions Jev answers

  • ChoiceWhich team should handle this?
  • ScoreHow urgent is it?
  • NoulDoes the customer ask for a refund?
  • NoulMust a human review it first?

Churn and escalation risk

Catch customers who are about to leave or escalate, before it happens.

Example scenario: Read customer messages and call notes and tell us how likely the customer is to cancel, whether they mention a competitor, how frustrated they are, and whether an account manager should call them this week.

Typical users: Customer success, account management

Questions Jev answers

  • ScoreHow likely are they to leave?
  • NoulDo they mention a competitor or alternatives?
  • ScoreHow frustrated do they sound?
  • NoulShould an account manager call this week?

Sales & marketing

Inbound lead qualification

Score website enquiries and send the good ones to the right sales rep.

Example scenario: We get enquiries through our website contact form. For each one decide what service they want, how well they fit our ideal customer, how soon they want to start, whether a budget is mentioned, and whether it's spam or a job seeker instead of a real lead.

Typical users: Sales, pre-sales and marketing ops

Questions Jev answers

  • ChoiceWhich service are they asking about?
  • ScoreHow well do they fit our ideal customer?
  • ChoiceWhen do they want to start?
  • NoulDo they mention a budget?
  • NoulIs this a genuine sales enquiry?

RFP and tender screening

Decide quickly whether an RFP is worth bidding on.

Example scenario: We receive RFPs and tender documents. Decide which service line they belong to, how well they match our capabilities, whether there are hard blockers (certifications, local presence, security clearance), how complex the scope is, and whether we should bid.

Typical users: Bid managers, pre-sales, business development

Questions Jev answers

  • ChoiceWhich service line does it fit?
  • ScoreHow well does it match our capabilities?
  • NoulIs there a requirement we can't meet?
  • ScoreHow complex is the scope?

Finance & risk

Expense and invoice checks

Categorise expenses and flag the ones that break policy.

Example scenario: Check employee expense claims and supplier invoices. Put each into an expense category, flag anything that breaks our expense policy, detect possible duplicates or personal spending, and decide whether it needs manager approval.

Typical users: Finance, accounts payable, spend management tools

Questions Jev answers

  • ChoiceWhich expense category is this?
  • NoulDoes it break the expense policy?
  • NoulDoes it look like a personal purchase?
  • NoulDoes it need manager approval?

Fraud and claim risk signals

Spot suspicious insurance claims, refunds or transactions for review.

Example scenario: Review insurance claim descriptions and supporting notes. Rate how suspicious each claim is, identify which fraud pattern it resembles, and check whether the story is internally consistent and whether key documents are missing.

Typical users: Risk, fraud and claims teams

Questions Jev answers

  • ScoreHow suspicious is this claim?
  • ChoiceWhich pattern does it resemble?
  • NoulAre there contradictions in the story?
  • NoulAre required documents missing?

HR & people

Resume screening

Check candidates against must-have requirements consistently.

Example scenario: Screen resumes for a Senior React Developer role. Decide the candidate's seniority, check each must-have requirement separately, rate the overall fit, and flag if the resume is for a different role entirely.

Typical users: Recruiters, hiring managers, ATS vendors

Questions Jev answers

  • ChoiceWhat seniority level is the candidate?
  • Noul4+ years of React experience?
  • NoulUses TypeScript?
  • ScoreHow well do they fit the role?

Employee request and feedback routing

Route HR requests and survey comments, and escalate sensitive ones.

Example scenario: Route employee HR requests and anonymous survey comments. Decide the topic, the sentiment, and whether the message describes harassment, safety or another sensitive issue that must go straight to a senior HR partner.

Typical users: HR operations, people analytics

Questions Jev answers

  • ChoiceWhat is the request about?
  • ScoreHow positive or negative is it?
  • NoulIs it a sensitive conduct or safety issue?

Content & community

Content moderation

Label user posts and comments for policy violations and severity.

Example scenario: Moderate user comments and listings on our marketplace. Decide which policy category a post falls under, how severe the violation is, whether it's spam or a scam, and whether it should be removed automatically or sent for human review.

Typical users: Marketplaces, communities, social apps

Questions Jev answers

  • ChoiceWhich policy category applies?
  • ScoreHow severe is the violation?
  • NoulIs it spam or a scam?
  • NoulIs it safe to remove automatically?

Review and survey insights

Turn product reviews and NPS comments into structured signals.

Example scenario: Analyse app store reviews and NPS survey comments. Tag the main topic, the sentiment, whether it's a bug report or a feature request, and whether the user mentions a specific feature by name.

Typical users: Product, marketing, research teams

Questions Jev answers

  • ChoiceWhat is the review mainly about?
  • ScoreHow positive is it?
  • NoulIs it reporting a bug?
  • NoulIs it asking for a new feature?

AI quality & safety

Guardrails for chatbot answers

Check every AI answer before users see it: grounded, on-topic, safe.

Example scenario: Our support chatbot answers customer questions using our help-centre articles. Before an answer is shown, check whether it is supported by the provided article, stays on topic, gives legal/medical/financial advice it shouldn't, has the right tone, and whether it should be handed to a human.

Typical users: AI product teams, anyone shipping an LLM feature

Questions Jev answers

  • NoulIs the answer supported by the article?
  • NoulDoes it answer the question asked?
  • NoulDoes it give restricted advice?
  • ScoreHow well does the tone fit our brand?

Intent routing for AI agents

Decide which tool or workflow an AI agent should run for each request.

Example scenario: Our in-app assistant receives free-text requests. Decide which workflow to run (search docs, create ticket, check order status, change account settings, talk to a human), whether the request needs a logged-in user, and whether it is ambiguous enough to ask a follow-up question.

Typical users: Teams building AI assistants and agents

Questions Jev answers

  • ChoiceWhich workflow should run?
  • NoulDoes it need a logged-in user?
  • NoulIs a follow-up question needed?

Operations

IT incident and alert severity

Classify incidents and alerts and decide who gets paged.

Example scenario: Classify incoming IT incident reports and monitoring alerts. Decide the affected system, the severity, whether customers are impacted, and whether the on-call engineer should be paged now.

Typical users: IT ops, SRE, managed-service providers

Questions Jev answers

  • ChoiceWhich system is affected?
  • ScoreHow severe is it?
  • NoulAre customers affected?
  • NoulShould on-call be paged now?

Document intake and verification

Identify uploaded documents and check they're complete and valid.

Example scenario: Customers upload documents during onboarding (we extract the text). Identify the document type, check whether the name matches the applicant, whether the document looks expired, and whether any required field is missing.

Typical users: Onboarding, KYC, back-office teams

Questions Jev answers

  • ChoiceWhat type of document is it?
  • NoulDoes the name match the applicant?
  • NoulIs it expired or too old?
  • NoulAre required fields missing?

Industry-specific

Healthcare: patient message triage

Route patient portal messages and flag urgent ones (no diagnosis).

Example scenario: Route patient portal messages to the right team (appointments, prescriptions, billing, clinical question) and flag messages describing symptoms that need a clinician to look at them today. The system must never diagnose.

Typical users: Clinics, telehealth and digital health apps

Questions Jev answers

  • ChoiceWhich team should handle it?
  • ScoreHow urgently should a clinician see it?
  • NoulDoes a clinician need to see it today?

Real estate: enquiry qualification

Sort property enquiries into buyers, renters and sellers, and rank them.

Example scenario: Qualify enquiries from property listings. Decide whether the person wants to buy, rent or sell, how ready they are, whether they mention financing or a mortgage approval, and whether they asked for a viewing.

Typical users: Agencies, property portals, proptech

Questions Jev answers

  • ChoiceBuy, rent or sell?
  • ScoreHow ready are they to act?
  • NoulDo they mention financing in place?
  • NoulDo they ask for a viewing?

E-commerce: returns and catalogue

Understand return reasons and keep product listings correctly categorised.

Example scenario: Classify return requests: the reason, what resolution the customer wants, and whether the item was damaged in transit. Also detect whether the complaint suggests the product description on the site is wrong.

Typical users: Online stores, marketplaces

Questions Jev answers

  • ChoiceWhy is it being returned?
  • ChoiceWhat does the customer want?
  • NoulWas it damaged in delivery?
  • NoulIs the product listing wrong?

Education: rubric-based grading

Score written answers against a rubric, one criterion at a time.

Example scenario: Grade short written answers against our rubric. Score each rubric criterion separately (argument, use of evidence, clarity), check whether the answer is on topic, and flag possible copied or AI-generated text for a teacher to check.

Typical users: Ed-tech, training and certification providers

Questions Jev answers

  • ScoreHow strong is the argument?
  • ScoreHow well is evidence used?
  • ScoreHow clear is the writing?
  • NoulDoes it answer the question?

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