> ## Documentation Index > Fetch the complete documentation index at: https://docs.typesafe.ai/llms.txt > Use this file to discover all available pages before exploring further. # Example use cases > Explore TypeSafe use cases by industry and turn promising ideas into software workflows. Use this map to brainstorm where TypeSafe could fit in your industry. Open the closest industry, scan the example decisions, and adapt them to the documents and actions in your own workflow. ## Example use case categories Interleave AI judgments with reliable software primitives. Code owns control flow; TypeSafe handles the narrow semantic decisions. Put a decision in the user interface or request path when latency matters. Most System One queries run in about 100 ms. Apply the same typed questions across a large corpus when general-purpose LLM cost makes the workload impractical. Judge LLM outputs, citations, extracted fields, or tool calls. Split “correct” into a set of observable checks. ## Example automation use cases * 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. * 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. * Classify incoming tickets by issue, product area, and customer intent. * 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. * 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. * 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. * 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. * Classify and normalize product listings across inconsistent seller catalogs. * Extract product attributes from titles, descriptions, and images. * Detect prohibited listings, counterfeit signals, review abuse, and policy violations. * Rank products and route uncertain listings for human review. * 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. * 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. * 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. * Convert incident reports, claims notes, transaction descriptions, and vendor assessments into consistent risk indicators. * Classify risk types and detect suspicious characteristics. * Score severity and prioritize review. * Extract features for broader risk models. * 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. * Retrieve items that match a natural-language query. * 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. * 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. * Evaluate model inputs and outputs before software acts on them. * Detect jailbreaks and prompt injection. * Identify policy violations and sensitive-data exposure. * Check tool-call errors and response-quality failures. * Route each request to the appropriate model or workflow. * Classify intent and domain. * Estimate difficulty and risk. * Escalate requests that need a more expensive model. ## Example task categories | 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 |