Grouped by industry. Each card explains how the integration works and walks through one example.
Jev (from TypeSafe AI) is not a text generator. It is a “System One” decision model: you give it context plus a bounded question, and it returns a typed answer with a probability. It is fast and cheap (roughly 170–180 ms median, output tokens free), text-only with a 64k request limit, and it gives no explanation.
That makes the sweet spot high-volume classification, scoring, and gating where code acts on the verdict, and low-confidence cases cascade to an LLM (for reasoning) or a person (for the decision). Every example below follows that pattern.
Each example shows the input your code assembles, the ask (primitive and answer space), what Jev returns (the winning answer with its probability), and what the surrounding code does next. Inputs are illustrative, not real records.
The intake system bundles the request form, the attached clinical notes, and the plan’s medical-necessity policy for that procedure into one text payload. Rather than one big question, Jev gets a Noul per criterion so each can be thresholded on its own. Requests where every criterion clears 0.9 go to the auto-approval queue; anything with a criterion under 0.6 goes straight to a nurse; the middle band gets an LLM-written summary so the human read is faster.
Remittance advice mixes CARC/RARC codes with free-text payer notes, and the same denial can be expressed a dozen ways. Jev reads the remittance text and picks a category from the appeals team’s working taxonomy. The category maps deterministically to an appeal template and a deadline clock. Low-probability picks are batched for a human to review together.
A rules engine already flags volume outliers; Jev adds a read of the narrative text (visit descriptions, provider notes, service justifications). It scores on a four-point scale using the signals investigators actually use: templated notes, impossible visit durations, services inconsistent with specialty. Only the top band goes to an investigator; the rest silently feeds the provider risk score.
Jev doesn’t carry the whole ICD-10 codebook, so a retriever or rule-based coder produces three to eight candidates from the note first. Jev then picks the best candidate, or “none of these,” given the note text. Coders see the choice pre-selected with its probability; anything under 0.75 is left blank so the coder isn’t anchored.
Secure-portal messages arrive as free text. Jev picks the destination queue from the plan’s list and a second Noul flags a time-sensitive need (medication running out, lost card before a procedure). Routing takes under a second, so the member gets an accurate “we’ve sent this to pharmacy” acknowledgment immediately.
For Medicare Advantage, an expedited appeal carries a 72-hour clock, so missing one is a compliance failure. Jev scores the appeal letter on urgency using the CMS definition (seriously jeopardizes life, health, or ability to regain maximum function). Intake sets the clock from the score band; borderline cases go to a clinician for the expedite decision.
Clinics are buried in portal messages. Jev answers a Noul “needs same-day clinician attention” and a Choice for who should see it first. Messages with a high same-day probability float to the top of the clinician inbox; scheduling and billing questions never reach the clinician at all.
Trial protocols are long lists of yes/no statements. Code splits the protocol into individual criteria, and for each patient Jev answers a Noul per criterion against the chart summary. A patient is a candidate only when every inclusion criterion is above threshold and every exclusion criterion is below it. Coordinators then chart-review only the candidates.
Before any templated or AI-drafted communication goes out, Jev checks “contains PHI belonging to someone other than the addressee” and “contains PHI not needed for its purpose.” Anything above a deliberately low threshold is held. At this price every message gets checked, not a sample.
Dispute forms and chat transcripts are free text; chargeback reason codes are not. Jev picks the dispute category from the bank’s internal list, which maps deterministically to the network reason code and the evidence checklist. The agent sees the mapped category pre-selected and can override it.
Uploaded documents are OCR’d; Jev classifies the document type from the OCR text, then answers a Noul per required element (name present, address present, expiry present and valid). Missing items trigger a re-upload request on the spot instead of a back-office rejection a day later.
Underwriters check that what the applicant typed matches what the documents say. Jev takes the application summary plus extracted document text and answers Noul statements like “stated employer matches the pay stub” and “stated income is within 10% of documented income.” Mismatches go to the underwriter with the specific statement that failed.
Transaction monitoring produces far more alerts than analysts can clear. Jev scores each alert narrative (pattern, customer profile, prior history) on investigative priority. The queue is sorted by score; the lowest band is auto-closed with documented rationale only when the probability is very high and the policy allows it.
Research teams want a consistent sentiment series, not prose summaries. Jev scores each transcript section (prepared remarks vs. Q&A) on a five-point tone scale, and a Noul flags “management lowered guidance.” The scores become features in quant models; an LLM is only used when an analyst asks why.
Banks must identify complaints that fall under specific regulatory categories (UDAAP, fair lending, Reg E errors). Jev reads each complaint and answers one Noul per category. High-probability hits are logged to the compliance system within minutes of receipt instead of at a weekly review.
Vendor-payment fraud usually starts with an email saying “we changed banks.” Jev reads inbound mail to accounts payable and answers “requests a change to payment details” and “conveys urgency or secrecy.” Anything above threshold requires an out-of-band callback before the change can be entered.
Before a generative assistant answers, Jev classifies the intent and flags whether the request touches money movement or account changes. Informational intents go to the LLM freely; high-risk intents route to an authenticated, scripted flow. The LLM never sees a transfer request without that gate.
FNOL arrives by phone transcript, web form, or email. Jev picks the claim type and a second Choice for the coverage line. The claim is created in the right system with the right fields before an adjuster touches it.
Simple claims should close in days. Jev scores the FNOL narrative on expected severity so low-severity claims go to straight-through processing and high-severity ones to senior adjusters. Score bands are re-tuned quarterly against actual settlement amounts.
Recovery dollars get missed when nobody notices another party was at fault. Jev reads the claim notes and police-report text and answers “a third party is likely responsible for the loss.” Claims above threshold are tagged for the subrogation team at creation rather than discovered at closing.
Application answers and broker notes are checked against a list of underwriting concerns. Jev answers one Noul per concern: undisclosed prior claims, business use of a personal vehicle, property vacancy. Flags route the application to a human underwriter; clean applications bind automatically.
Coverage questions from agents and insureds reference specific parts of the policy form. Jev picks the relevant section so the question lands with the specialist who owns it, instead of bouncing between teams.
QA teams hand-review a few percent of files. Jev scores every claim note against the documentation rubric (contact logged, coverage confirmed, reserve rationale, next action). Supervisors coach from the low-score tail instead of random samples.
Sellers submit items with inconsistent titles and descriptions. Jev picks the category level by level (department, then category, then subcategory), each as a Choice over the valid children. At sub-cent cost the whole catalog can be re-run whenever the taxonomy changes.
Reviews are checked before publishing. Jev answers “violates review policy” and, if so, a Choice for the reason. High-probability violations are auto-rejected with the reason shown to the customer; the middle band goes to a moderator.
Customers write “didn’t fit,” “runs small,” “too tight in the shoulders,” which are all the same thing to merchandising. Jev maps the free text to the fixed reason list so return analytics are consistent per SKU. Sizing complaints above a rate threshold auto-flag the product page for a size-guide update.
Brand-protection teams can’t read every listing. Jev scores listing text on counterfeit likelihood using signals like implausible pricing, misspelled brand names, “authentic” claims, and stock-photo language. The top band goes to the brand for a test buy; the score is an input, not the verdict.
The search engine should behave differently for “return policy” than for “running shoes.” Jev classifies each query’s intent so the front end shows a help answer, a product grid, or a comparison layout. Latency matters here, which is why a generative model isn’t in the path.
For attributes with closed vocabularies, Jev reads the product copy and picks one value per attribute. Each attribute is a separate Choice in the same request, with “unknown” as an option so it never guesses. Storefront filters become reliable.
Agents spend minutes reading competitor screenshots against the policy. Jev takes the request text plus the policy and answers one Noul per condition (identical model, in stock, authorized retailer). Clear passes are approved automatically; the rest go to an agent with the failing condition highlighted.
Jev picks the owning team from the help desk’s queue list using the ticket subject and body. Because it returns a probability, tickets with no confident team go to a general triage queue instead of being misrouted and bounced.
Transcripts are scored on frustration and a Noul flags “customer mentions leaving or cancelling.” Supervisors get a live list of conversations sliding toward churn, and retention offers are gated on the score rather than on an agent remembering to escalate.
Instead of relying on agent judgment, Jev answers “this conversation meets the escalation criteria” using the company’s own list (legal threat, safety issue, repeated contact, executive complaint). The chat tool surfaces an escalate button automatically when the probability clears threshold.
Survey response rates are low and skewed. Jev scores every closed conversation on predicted satisfaction from the transcript, giving a complete signal per agent and per issue type. Teams calibrate the score against the surveys they do receive.
QA rules are yes/no: did the agent identify themselves, read the required disclosure, avoid promising a refund they can’t authorize. Jev answers one Noul per rule for every transcript. QA analysts review the fails rather than listening to random calls.
When a new ticket arrives, search finds the customer’s recent tickets and Jev answers “this is the same underlying issue” for each candidate. Confident matches are merged automatically so the customer isn’t answered twice by two agents.
Not every request needs a frontier model. Jev classifies the request’s difficulty tier and the harness sends it to the cheapest model that fits. At under 200 ms the router adds almost nothing to latency and pays for itself quickly.
Given the current state and the user’s last message, Jev picks the next tool from the harness’s registered list. The tool’s arguments are still produced by code or an LLM, but the branch decision is fast, thresholdable, and can never name a tool that doesn’t exist.
Every proposed tool call passes through “this action is irreversible or externally visible” before execution. Deletes, payments, and outbound messages clear the threshold and pause for approval; reads and drafts don’t. This is middleware, not a prompt instruction, so the main model can’t be talked out of it.
The runtime lists the page’s interactive elements with indexes; Jev chooses which element to act on and which operation to perform given the task. An LLM plans, Jev executes each bounded step quickly, and the runtime performs the action.
On open, Jev labels the PR type from its title and description and scores review risk (touches auth, migrations, payment code). Labels drive reviewer assignment; the risk score decides whether one or two approvals are required.
Alerts from many systems arrive in different formats. Jev assigns severity from the alert text and answers “same incident as the currently open alert” for dedupe. On-call engineers see one incident instead of forty notifications.
The harness hands Jev the last N steps of an agent’s trace and asks “the agent is repeating actions without progress.” A high probability trips a circuit breaker that stops the run and asks the user, saving tokens and preventing runaway loops.
For every RAG answer, Jev receives the retrieved passages and the generated answer and answers “every factual claim is supported by the passages.” Because it runs in under 200 ms it can sit in the response path, not only in offline evals. Low-probability answers are regenerated or shown with a warning.
Eval suites with thousands of cases become affordable to run on every commit. Each rubric dimension is a Score in the same request, so one call returns helpfulness, tone, and completeness with probabilities. Regression is flagged when the distribution shifts, not only the mean.
When comparing two model versions, Jev picks the better of two responses to the same prompt against stated criteria. Over a large prompt set this yields a win rate with confidence intervals quickly and cheaply; disagreements with sampled human labels are investigated.
User text and retrieved documents are checked with “contains instructions aimed at the model rather than content for the task.” It runs before the main model sees anything, so it can’t be bypassed by the main model being persuaded. Hits are stripped or the request is refused.
A policy is decomposed into clauses (no medication dosages, no financial guarantees, no PII echo). Jev answers one Noul per clause for every response before it is shown. The clause that fired is logged, which makes policy tuning concrete.
Jev grades everything; any verdict with probability between 0.3 and 0.7 is escalated to an LLM judge that can reason and explain, and residual disagreements go to a person. Cost stays near the Jev price while the hard cases still get real scrutiny.
Red-team runs produce thousands of responses to adversarial prompts. Jev classifies each as refused, partial, or full compliance so attack success rates are computed consistently across runs and model versions.
Instead of sampling 1% of traffic for offline review, every production answer is checked for support against its sources and the results are charted by feature and by hour. A spike becomes visible in minutes rather than at the next weekly review.
Contracts are split into clauses by a parser; Jev labels each with its type from the firm’s taxonomy. The result powers clause search across the repository and routes each clause to the right playbook check.
For each classified clause, Jev compares it to the firm’s standard position and answers “departs from the playbook” plus a Score for how material the departure is. Negotiators see a redline-priority list rather than reading the whole document.
Document review is the most expensive part of litigation. Jev answers “responsive to request #3” and “likely privileged” per document. Reviewers work the uncertain band first; the confident bands are validated by sampling, with the cutoffs defended in the review protocol.
Regulatory feeds publish hundreds of updates a week. Jev picks which business unit each update affects and a Noul flags “requires a policy change.” Compliance officers get a filtered digest rather than the raw feed.
Data-subject requests arrive by email and web form in many wordings. Jev classifies the request type so the right workflow and the statutory clock start on day one. When two types are close, both workflows open and an agent confirms with the requester.
Marketing drafts are checked against the compliance rule list before legal sees them. Jev answers one Noul per rule (unsubstantiated claim, missing disclaimer, comparative claim naming a competitor, health claim). Copy that passes everything goes to publish; the rest goes to legal with the flagged sentence.
Technician notes are short and jargon-heavy. Jev picks the failure mode from the maintenance taxonomy so reliability engineers get clean Pareto charts of what is actually breaking. Classification happens at work-order close-out, not in a quarterly cleanup.
Procurement inboxes receive hundreds of supplier emails a day. Jev picks the intent and a Noul flags “affects a delivery date.” Delay notices surface to planners immediately instead of waiting for a buyer to get to them.
Nonconformance reports are scored for containment urgency using the plant’s severity definitions (customer impact, safety, regulatory). High-severity reports page the quality manager; the rest enter the normal CAPA queue.
Employees file safety reports in free text; recordability depends on the event type. Jev classifies the type so near-misses feed the leading-indicator program and recordables start the regulatory process immediately.
Requisitions describe parts loosely. Search returns candidate catalog entries, and Jev picks the matching one or “none” given the description and the candidates. Buyers approve matches above threshold in bulk.
Carrier scan notes and customer messages explain delays in different words. Jev normalizes each exception to a reason code so customer notifications and carrier scorecards are consistent across carriers.
Before dispatch, Jev reads the address lines and delivery instructions and answers “deliverable as written” (missing unit number, PO box for a freight shipment, contradictory instructions). Problem addresses get a customer confirmation request before the truck leaves.
Commercial invoices and packing lists are OCR’d; Jev answers one Noul per required field (HS code present, country of origin stated, value declared, incoterm named). Shipments missing anything are fixed before tender, not at the border.
Driver incident reports are scored on severity using the fleet’s definitions so safety managers see the serious ones first and claims gets the ones with third-party damage. The score also feeds the driver coaching program.
Quote requests arrive as emails. Jev picks the service level and the mode so the quoting system can price immediately; ambiguous requests get a clarifying reply from an agent instead of a wrong quote.
Each must-have requirement is a separate Noul against the résumé text (“holds a valid RN license,” “5+ years in payer operations”). Nothing is rejected automatically; the checks reorder the recruiter’s review queue, and the thresholds are audited for adverse impact across groups.
Candidates apply to generic postings or the wrong one. Jev picks the best-fit open requisition from the candidate’s summary so the right recruiter sees them. The candidate’s original choice is preserved.
Open-ended survey comments are scored for sentiment and tagged with a theme from the HR taxonomy. Leaders get theme counts and sentiment by team within hours of the survey closing, with the comments themselves still anonymous.
HR help-desk questions are routed to the right specialist team. Jev classifies the topic and a Noul flags “time-sensitive” (leave starting this week, payroll error). Routing is instant and the acknowledgment tells the employee who has it.
Employee-relations intake must separate urgent reports from general grievances without someone reading everything first. Jev scores urgency using the company’s escalation definitions. High scores page the on-duty ER partner; the content is scored, never summarized, by the model.
Exit-interview notes are tagged with a primary and secondary departure reason. Aggregated by team and tenure band, the tags show where attrition is driven by pay versus management versus growth.
At ad-auction time there is no room for a generative call. Jev reads the page text and answers “unsuitable for this advertiser” against the advertiser’s exclusion list. Bids are suppressed on pages above threshold.
Every article, episode, or clip gets tags from the recommendation taxonomy. Jev picks topic, tone, and audience level as separate Choices. Consistent tags mean better recommendations and cleaner content reporting.
Chat messages are checked at tens of thousands per minute. Jev returns a harm probability and a violation category; the platform hides messages above threshold and routes borderline ones to human moderators.
Inbound form text (“tell us about your project”) is scored for fit against the ideal customer profile. Sales follows the high-score leads within minutes; low scores get the nurture sequence.
Before running a live A/B test, Jev picks the predicted winner between candidate headlines for a given audience. Editors still run live tests for close calls, but obvious losers are cut without spending impressions.
Brands want to reuse customer posts but need them to meet reuse criteria (no competitor products, disclosure present, no minors). Jev answers one Noul per criterion against the caption and post text; passing posts go to a person for the final image check.
For constrained-response questions, Jev scores each answer against the rubric levels. Teachers see confident scores pre-filled and only read the uncertain ones. The rubric text travels in the request, so a changed rubric regrades instantly.
Questions in a course help channel are routed to an FAQ bot, a tutor, or an advisor. Jev picks the destination and a Noul flags “student expresses distress,” which always goes to a person.
Submissions are checked with “writing style is inconsistent with this student’s prior work” and “contains text matching the provided sources verbatim.” Flags only prioritize instructor review; no student is accused on the model’s say-so.
End-of-term comments are tagged by theme so departments can compare courses on the same dimensions. Jev picks the theme and scores sentiment for each comment.
Customer reports from the app, SMS, and IVR transcripts are classified by service type and likely cause so dispatch can group them into events. Grouping happens in real time during storms when volume spikes.
Inspection notes on poles, transformers, and meters are scored for asset condition. Scores feed the maintenance planning model, replacing a yearly manual read of the notes.
NOC tickets are classified by root-cause category from the engineer’s notes so recurring problems are visible. Jev picks the category at ticket close; a Noul flags “customer-impacting.”
Support chats are scored for churn risk and a Choice captures the reason (price, reliability, competitor offer). Retention offers are targeted to the reason rather than a generic discount.
Long rate-case and compliance filings are split into sections and tagged so analysts’ and regulators’ questions can be matched to the right section quickly. Jev picks the section type from the filing taxonomy.
Applications are checked for completeness before a caseworker opens them. Jev answers one Noul per required element (proof of income attached, household members listed, signature present). Applicants get a same-day request for missing items rather than a denial weeks later.
Non-emergency requests are routed to the responsible department from the resident’s description. Jev picks the department and a Noul flags “possible safety hazard” for faster handling.
Public-records requests are categorized and a Noul estimates whether responsive records likely contain exempt material (personnel, ongoing investigation, security). The flag routes the request to legal review up front rather than after the records are pulled.
Proposed rules draw thousands of comments. Jev tags each comment with a theme from the agency’s list and a Choice for position. Staff get theme counts within hours and can read representative comments per theme.
Residents describe projects in plain language; Jev picks the permit type so the correct application and fee schedule are presented immediately. A Noul flags “may require multiple permits.”
Portfolio managers review hundreds of leases. Each clause is scored on risk against the company’s standards (assignment restrictions, relocation rights, CAM caps). The highest-risk clauses across the portfolio surface in one list.
Home and commercial inspection findings are scored for severity so buyers and asset managers see the big issues first. Jev scores each finding using the inspector’s own text.
Requests for information are routed to the right discipline on the design team. Jev picks the discipline and a Noul flags “schedule-critical.” The right engineer sees it the same day instead of after a project manager’s triage.
Reported and inbound emails are scored with “this is a phishing attempt” using the message text and a header summary. It supplements the email gateway rather than replacing it: user-reported emails get an instant verdict back, and confirmed phish are pulled from every mailbox.
SOC alerts are re-scored on severity using the alert text and asset context, cutting through vendor-assigned severities that are often inflated. Analysts work the Jev-ranked queue; the vendor severity is kept for audit.
Access requests include a free-text justification. Jev answers “the justification is valid for this role and system” against the role’s documented need. Clear approvals go through; weak justifications go back to the requester’s manager.
Incidents are categorized from the initial report so the right playbook and escalation path start immediately. Jev picks the category and a Noul flags “data exfiltration suspected,” which starts the legal and privacy notification clock.
Nothing matches that filter.