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Use this map to find places where a structured, probabilistic decision can replace a brittle prompt or manual review step. Open the closest workflow, adapt the example to your own policy and data, then use the GLiDE Inference guide for the request contract. These are decision patterns, not built-in end-to-end workflows. Your application supplies one case and owns the downstream action. Ask an independent question for each property that can coexist. To rank a collection, score each item separately and sort the results in application code.

AI agents and automation

Tool approval, routing, output verification, and context selection.

Customer and revenue operations

Support triage, lead qualification, and marketplace workflows.

Trust, safety, and compliance

Moderation, prompt security, policy review, and risk operations.

Research, data, and product

Research screening, product signals, graph quality, and semantic checks.

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AI agents and automation

  • Decide whether a proposed tool call is allowed under the current policy.
  • Route an action to execute, revise, request approval, or stop.
  • Score the risk of sending an email, changing data, spending money, or publishing content.
  • Check whether the visible evidence supports the agent’s stated reason for acting.
  • Choose the best model, agent, tool, or workflow for each request.
  • Classify intent, domain, complexity, and risk before selecting a route.
  • Decide whether a request needs a specialist or more capable fallback.
  • Route uncertain cases to a human instead of forcing an automated path.
  • Check whether a response follows the user’s instructions and required format.
  • Evaluate whether supplied evidence supports a claim or citation.
  • Check each required step and detect unsupported conclusions or source conflicts.
  • Score an output against a product-specific quality rubric.
  • Decide whether a passage is relevant enough to include in an AI context window.
  • Score query-to-document relevance before generation.
  • Detect conflicting or stale context that should be excluded.
  • Score candidate records individually, then sort them for retrieval or review.

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Customer and revenue operations

  • Route tickets by product area, issue type, account tier, or customer intent.
  • Score urgency, frustration, churn risk, or operational impact.
  • Check refund, replacement, or escalation eligibility against a supplied policy.
  • Verify that a proposed support response addresses the request and follows policy.
  • Evaluate whether an account matches an ideal customer profile.
  • Classify purchase intent, use case, company maturity, or sales motion.
  • Score fit, urgency, and likelihood of near-term engagement.
  • Route leads to self-serve, sales, partnerships, or nurture workflows.
  • Classify listings into the correct catalog or review queue.
  • Check whether a listing violates marketplace or advertising policy.
  • Score counterfeit, review-abuse, or seller-risk indicators.
  • Route uncertain listings to a specialist without blocking the full catalog.

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Trust, safety, and compliance

  • Check whether text violates a supplied rule for harassment, spam, fraud, or unsafe advice.
  • Classify the primary policy category for a moderation case.
  • Score severity and decide whether to allow, warn, review, or block.
  • Detect opt-out requests, personal-data exposure, or regulated claims.
  • Detect prompt injection, jailbreak attempts, or requests to expose private data.
  • Check whether a tool call exceeds the user’s authorization or the agent’s scope.
  • Score the risk of retrieved instructions before adding them to agent context.
  • Route suspicious activity to logging, isolation, or human review.
  • Triage transaction narratives, claim notes, KYC records, or incident reports.
  • Classify risk type, claim complexity, or missing-information status.
  • Score fraud indicators, evidence quality, or review priority.
  • Route ambiguous cases to an investigator, adjuster, or compliance specialist.

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Research, data, and product

  • Screen papers against explicit inclusion and exclusion criteria.
  • Check whether a passage supports a research claim.
  • Check separately for required controls, dataset details, and methodological information.
  • Check each predefined theme against interview and survey passages.
  • Classify feedback by product area, request type, or customer segment.
  • Detect purchase intent, competitive pressure, supply concerns, or churn signals.
  • Score sentiment intensity, urgency, and likely business impact.
  • Turn probabilities into features for a forecasting or prioritization model.
  • Classify a proposed relationship between known entities.
  • Check whether a claim contradicts an existing record.
  • Score confidence in a candidate edge before adding it to a graph.
  • Route ambiguous relationships to a verification workflow.
  • Check generated code against team-specific architectural conventions.
  • Detect whether a pull request is missing tests, migration steps, or documentation.
  • Score a document against a writing, accessibility, or brand rubric.
  • Run lightweight semantic checks in CI before requesting human review.
Use GLiDE as decision support, not as the sole authority for irreversible or high-impact decisions involving safety, legal rights, employment, credit, healthcare, or access control. Validate thresholds on representative data and provide a human review path.
GLiDE evaluates supplied context; it does not extract text spans or generate prose. Use GLiNER for entity and relationship extraction, and a generative model for open-ended writing.