Use cases
Use Modaic for repeatable decisions with a clear output and a human standard. Common examples include classifying content, scoring leads, routing tickets, and reviewing AI outputs.
What makes a good use case?
A task is a good fit when:
- The decision is bounded. The output is a label, score, selection, or structured field—not an open-ended response.
- The criteria can be written down. A policy, rubric, or expert judgment defines the correct answer.
- A human can review it. Experts can confirm or correct the model's answer.
- It repeats at scale. The same decision applies across many records, messages, documents, or model outputs.
Modaic is not intended for open-ended chat, long-form generation, or autonomous workflows without a specific decision to evaluate.
Types of decision tasks
| Type | What it does | Examples |
|---|---|---|
| Classification | Assign a label | Content policy, ticket category, medical code |
| Scoring | Apply an ordered rubric | Lead quality, risk, severity, answer quality |
| Selection | Choose an action or item | Approve, escalate, reject, next best action |
| Routing | Choose a destination | Support queue, claims team, sales owner |
| Matching | Compare two records | Duplicate detection, entity resolution, candidate fit |
| Verification | Check against a standard | LLM-as-a-judge, policy compliance, SOP adherence |
Common use cases
| Team | Decisions |
|---|---|
| AI product | Evaluate agent outputs, detect hallucinations, route uncertain results |
| Trust and safety | Classify violations, prioritize reports, apply moderation policy |
| GTM | Qualify leads, score accounts, review outbound messages |
| Customer support | Categorize tickets, set priority, choose the right queue |
| Human data | Grade training data, verify labels, apply quality gates |
| Compliance | Check policies, review access requests, detect sensitive data |
| Legal | Classify contract risk, identify clauses, route documents for review |
| Health | Review notes, check coding, route cases with appropriate oversight |
Start with one decision that your team already reviews manually. Define the possible outputs and criteria, then make your first decision.
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