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Use-case map by industry

[ concept ][ updated 2026-09-20 ][ confidence high ][ jev-1.13.0 ]#use-cases · industry · patterns · ideation

TL;DR Find the closest row below, steal the example decisions, and turn each one into a narrow typed question. The last table (decision shapes) is the fastest route from a vague idea to a primitive: match your problem to a shape, then pick Choice, Score, or Noul.

What it is

TypeSafe's map for brainstorming where a System One model fits. The docs' instruction: "Open the closest industry, scan the example decisions, and adapt them to the documents and actions in your own workflow."

Capability categories

Category What it means
AI Automation Software Interleave AI with reliable software so you can run it a million times in the background without a human co-pilot. Code owns control flow (not markdown files) while TypeSafe handles the semantic decisions and language understanding.
Real-time applications Frontier intelligence at real-time speeds (150 ms) means AI can make decisions faster than human perception. Fast and smart enough to be programmed to play games or embedded into a UI.
AI Map Reduce over Big Data 100x cheaper means you can process giant datasets: search relevant information over giant corpuses, classify giant agent traces, extract features to make predictions.
Universal Verification Verify the input prompt, extractions, reasoning traces, tool calls, or inputs of any other AI. Detect jailbreaks, citation errors, hallucinations, mistakes, or other error modes that other AIs or LLMs make, at a fraction of the cost of the actual LLM call.
Harness Engineering Use Jev queries to make your harness smarter — model routing, semantic context retrieval, LLM error detection and guardrails, reasoning-trace classification at lightspeed and a fraction of the cost.

Note the "150 ms" and "100x cheaper" figures here are TypeSafe's marketing shorthand on this page; the shipped numbers are $0.042 / MTok input with free output and a 70 ms–500 ms end-to-end range. See Models, aliases, pricing, rate limits, context and Workflow evals: how TypeSafe measures Jev.

Industry and function map

Every bullet below is verbatim from the source page, grouped by its accordion.

Area Example decisions
Search and retrieval Replace or supplement embeddings in RAG pipelines with semantic search, scoring, and ranking. • Score query-to-candidate relevance. • Rerank results with pairwise comparisons. • Cross-encode queries and candidates for higher precision. • Select useful context for downstream AI workflows.
Scientific discovery Screen papers against inclusion and exclusion criteria for systematic reviews. • Label passages in interview transcripts, open-ended survey responses, and field notes using predefined themes or categories. • Check whether cited passages support claims in manuscripts and generated summaries. • Flag missing methodological details, such as controls, dataset descriptions, and experimental settings. • Identify entities and relationships across papers to build research knowledge graphs, linking findings to supporting passages.
Model routing Use Jev to build a custom router that chooses which LLM receives each prompt. • Set routing rules and thresholds for your specific workflow. • Classify intent and domain. • Estimate difficulty and risk. • Escalate requests that need a more expensive model.
LLM guardrails Place semantic checks on every LLM input, output, and tool call at a fraction of the cost of the LLM call. • Detect jailbreaks and prompt injection. • Identify policy violations and sensitive-data exposure. • Detect tool-call errors and response-quality failures in real time. • Log structured check results and probabilities to make AI system and harness failures easier to trace.
Semantic code linting Use Jev queries to add automated semantic lints to code and writing. • Define checks for your team's coding conventions and writing guidelines. • Run these checks in CI and flag violations for review.
Feature extraction for predictive modeling Use Jev to extract probabilistic features from natural-language data. • Combine these features with structured data to train models for tasks with ground-truth outcomes. • Use autoresearch workflows to propose feature definitions and evaluate their predictive value against held-out ground truth.
Recruiting Evaluate resumes, applications, and interview feedback against explicit, job-related criteria. • Identify relevant experience. • Score evidence for required competencies. • Match candidates to roles. • Route candidates to hiring managers or recruiters. • Escalate uncertain cases for human review.
Lead generation Match company profiles, executive biographies, and inbound messages to an ideal customer profile. • Score industry fit and company maturity. • Detect buyer relevance, pain points, and purchase intent. • Prioritize and route leads.
Customer support Classify incoming tickets by issue, product area, and customer intent. • Process call transcripts to extract customer issues, commitments, and follow-up actions. • Detect urgency, frustration, churn risk, and refund requests. • Route cases to the right team, queue, or automated workflow. • Verify support responses against policies and the customer's request.
Insurance claims Classify first-notice-of-loss reports, adjuster notes, and supporting documents. • Detect claim complexity, missing information, and potential fraud indicators. • Prioritize claims for straight-through processing or specialist review. • Escalate uncertain or high-risk cases to a human adjuster.
Financial crime Evaluate transaction narratives, KYC documents, and alert histories for suspicious characteristics. • Match entities across inconsistent names, profiles, and records. • Prioritize alerts by risk, relevance, and evidence quality. • Route ambiguous cases to investigators for review.
Legal and compliance Classify contracts, policies, regulatory filings, and marketing claims. • Detect missing clauses, prohibited claims, and policy violations. • Verify documents against explicit legal or compliance requirements. • Escalate high-risk or uncertain findings to counsel or compliance teams.
E-commerce marketplaces Classify and normalize product listings across inconsistent seller catalogs. • Extract product attributes from titles and descriptions. • Detect prohibited listings, counterfeit signals, review abuse, and policy violations. • Rank products and route uncertain listings for human review.
Moderation and trust and safety Apply company-specific, nuanced criteria to decide which posts meet your moderation standards. • Moderate user content and automated conversations across communities, customer support, and SDR workflows. • Detect toxicity, harassment, spam, fraud, unsafe advice, personal-data exposure, opt-out requests, and policy-violating claims. • Combine severity and confidence to allow, warn, review, or block content.
Advertising Evaluate creative assets, campaign copy, landing pages, and placement context. • Classify brand safety and audience suitability. • Check regulatory compliance and prohibited claims. • Evaluate creative quality and ad-to-landing-page alignment.
Gaming Evaluate player reports, in-game chat, reviews, and support conversations. • Moderate chat and detect abuse, toxicity, or suspicious behavior. • Annotate content and score frustration or engagement. • Detect churn signals and route player-support requests.
Risk assessment Convert incident reports, claims notes, transaction descriptions, and vendor assessments into probabilistic risk indicators. • Use these indicators in insurance and underwriting workflows. • Classify risk types and detect suspicious characteristics. • Score severity and prioritize review. • Extract features for broader risk models.
Demand forecasting Enrich forecasting models with semantic signals from customer inquiries, sales notes, product reviews, support tickets, and market reports. • Extract purchase intent, urgency, and product interest. • Detect supply concerns, competitive pressure, and emerging demand themes. • Feed those features into a forecasting model alongside historical time-series data.
Graphs and knowledge graphs Annotate and verify knowledge graphs with typed semantic decisions. • Classify relationships and entity types. • Detect contradictions between records or claims. • Support probabilistic traversal and hierarchical classification.

Decision shapes (the fastest lookup)

Decision shape Reach for it when Examples
Classification One known category should win Intent, topic, department, risk type, entity type
Detection You need a probability that one property is present Spam, fraud, urgency, jailbreaks, sensitive data
Scoring The answer belongs on an ordered rubric Severity, relevance, quality, frustration, suitability
Routing A category selects the next code path Tool use, escalation, model routing, support queues
Search You need to find items that match a natural-language query Semantic search, document discovery, candidate generation
Retrieval A workflow needs the most relevant context or records RAG context, evidence retrieval, knowledge lookup
Ranking Items need to be ordered by semantic relevance or quality Search results, recommendations, candidate prioritization
Verification An artifact must be checked for specific failure modes Citation support, policy violations, tool-call errors, response quality
ML Feature Extraction A downstream classical ML model needs semantic signals Purchase intent, product interest, competitive pressure, churn signals
Structured Data Extraction Known fields must be recovered from unstructured input Candidate attributes, order fields, document labels

Why it matters for code

The shape tells you the primitive (inferred mapping — the source page does not state it explicitly): Classification and Routing → Choice; Detection and Verification → Noul; Scoring, Ranking, and Search relevance → Score; Structured Data Extraction and ML Feature Extraction → a fan-out of several of the above, one question per field or feature. See Choosing between Choice, Score, Noul.

Several of these areas have a distilled cookbook: Cookbook: Line-by-line search, Cookbook: Re-ranking, Cookbook: Classifying RAG passages, Cookbook: Double-checking citations, Cookbook: Guardrails for LLMs, Cookbook: Hierarchical classification, Cookbook: Knowledge graph entity alignment, Cookbook: Autoresearch feature discovery, Cookbook: Skill suggestion.

Gotchas

Related

Sources