AI Observatory
Review Queue
The review queue turns low-confidence answers, flagged support cases, store metadata suggestions, and safety issues into admin-reviewed improvements.
Public surface
Review Queue
AI should learn through review, not by blindly absorbing every interaction. The review queue gives CRYA a place to approve, edit, reject, or document repeated issues.
Low rating, unsafe topic, missing docs, policy concern, or repeated failure.
Suggested docs updates, prompt edits, store metadata changes, and support templates.
Approve, edit, reject, defer, or escalate to app/backend work.
Keep who reviewed what, why, and what changed.
| Queue sources | Assistant ratings, Telegram flags, support tickets, package failures, creator metadata, and admin notes. |
|---|---|
| Outcomes | Knowledge update, docs task, prompt change, support template, app bug, store review, or no action. |
| Privacy | Private support and account details stay protected and should not become public docs directly. |
| Quality loop | Review outcomes improve docs, prompts, support routes, and release planning. |
Workflow
How this should work
A public page should explain the user path while protected state, permissions, and audit trails stay in CRYA Mission Control.
A user, bot, support event, or admin marks something for review.
Admin classifies risk and product area.
Approve, edit, reject, or escalate.
Update docs, prompt, app copy, or support template.
Related surfaces
Continue the journey
Move between public discovery, creator guidance, store trust, and support evidence without exposing private admin systems.