## Example use case categories ### AI Automation Software Interleave AI with reliable software in a way where 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 (150ms) 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 for relevant information over giant corpuses, classify giant agent traces, and 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 for 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. ## Example automation use cases ### 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. ### 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.