Decision Scoring API Use Case
Problem
Section titled “Problem”A match score alone is not enough to decide whether a workflow should automate, review, or reject a result.
How AvelinLabs Helps
Section titled “How AvelinLabs Helps”AvelinLabs returns decision-support fields that help applications interpret result strength. Confidence, trust, uncertainty, ambiguity, and quality fields can be used to route outputs according to a product’s own policy.
Input Signals
Section titled “Input Signals”Decision scoring may consider signals such as:
- result confidence
- trust or quality indicators
- uncertainty and ambiguity
- strength of the top ranked result
- supporting skill or occupation evidence
Decision Output
Section titled “Decision Output”The current beta documentation describes decision labels such as:
AUTO_ACCEPTREVIEWREJECTAMBIGUOUS
These labels are intended to help downstream systems decide what should happen next. They should be combined with product-specific review and risk policies.
Product Fit
Section titled “Product Fit”Decision scoring can support analyst review queues, workflow routing, quality control, dashboards, and human-in-the-loop automation.
GitHub Example Reference
Section titled “GitHub Example Reference”Complete executable examples should live in the official AvelinLabs API examples repository.