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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.

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Example use case categories

Smart software

Interleave AI judgments with reliable software primitives. Code owns control flow; TypeSafe handles the narrow semantic decisions.

Real-time applications

Put a decision in the user interface or request path when latency matters. Most System One queries run in about 100 ms.

AI map-reduce

Apply the same typed questions across a large corpus when general-purpose LLM cost makes the workload impractical.

Verify everything

Judge LLM outputs, citations, extracted fields, or tool calls. Split “correct” into a set of observable checks.

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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 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.

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Example task categories