Datasets:
case_id stringclasses 6
values | lead dict | qualification dict | expected_decision stringclasses 5
values |
|---|---|---|---|
hot_001 | {
"consent_to_contact": true
} | {
"score": 92,
"confidence": 0.94,
"risk_flags": []
} | AUTO_ROUTE |
review_001 | {
"consent_to_contact": true
} | {
"score": 72,
"confidence": 0.89,
"risk_flags": []
} | HUMAN_REVIEW |
research_001 | {
"consent_to_contact": true
} | {
"score": 90,
"confidence": 0.62,
"risk_flags": []
} | RESEARCH_MORE |
risk_001 | {
"consent_to_contact": true
} | {
"score": 95,
"confidence": 0.98,
"risk_flags": [
"regulated_data"
]
} | HUMAN_REVIEW |
consent_001 | {
"consent_to_contact": false
} | {
"score": 96,
"confidence": 0.99,
"risk_flags": []
} | BLOCK |
nurture_001 | {
"consent_to_contact": true
} | {
"score": 48,
"confidence": 0.91,
"risk_flags": []
} | NURTURE |
Autonomous Revenue Ops — Evaluation Dataset
Synthetic regression cases for validating deterministic revenue-operations policy behavior.
Dataset purpose
The dataset checks that structured qualification outputs map to the expected authorized workflow state. It is designed for regression testing, not for training a foundation model.
Covered behaviors
- high-score, high-confidence autonomous routing
- medium-score human review
- low-confidence evidence collection
- risk-flag override
- consent-based blocking
- lower-score nurture routing
Schema
Each JSONL row contains:
case_idlead.consent_to_contactqualification.scorequalification.confidencequalification.risk_flagsexpected_decision
Data provenance
All records are synthetic and created for evaluation. They do not contain customer data or real personal data.
Companion artifacts
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