Scopegrade sample output
This is a fictional, redacted example. It shows the style and decision quality of a Scopegrade written review without using client data, testimonials, private systems or personal reputation.
A small B2B services team wants to automate intake triage. New requests arrive through email and a form, then staff manually classify priority, check missing fields, create a ticket, and draft the first response. They are considering an AI workflow plus RAG over internal service notes.
Narrow before build
The workflow is worth a small pilot, but not a full automation sprint yet. The first funded milestone should handle classification and missing-field detection only. Drafted client replies should stay human-approved until evaluation data is stronger.
The source material is usable for internal triage, but not clean enough for customer-facing answers. The team needs 30-50 reviewed examples before expanding into reply drafting.
| Milestone | Acceptance check | Do not include yet |
|---|---|---|
| 1. Intake normalizer | Extract account, request type, urgency, missing fields from 25 redacted examples. | Any customer-visible response. |
| 2. Human approval queue | Staff can approve/correct labels and missing-field notes in one place. | Auto-close, billing changes or refund actions. |
| 3. RAG evidence panel | Show 2-4 cited internal notes relevant to the ticket category. | Generated final answer without review. |
Buy the smallest review or implementation slice that proves the triage loop: classify, cite evidence, surface missing fields, and collect staff corrections. If that review queue reduces handling time without increasing rework, expand into response drafting later.