Bluemoonkey / Community / AI Support Triage

AI Support Triage App Blueprint

A complete architecture for an AI-powered customer support triage system — sentiment classification, priority scoring, auto-reply drafting, escalation checkpoints, and a human approval gate before replies go out. 12 nodes, ready to export to Cursor or Claude Code.

SaaS Support automation 12 nodes
Table of contents
  1. What this app does
  2. Architecture diagram
  3. Node breakdown
  4. How it works end-to-end
  5. Cursor export prompt
  6. FAQ

What this app does

An AI support triage system sits between your customers and your support team. When a ticket comes in — by email, form, or chat — the AI classifies its sentiment and urgency, drafts an appropriate reply, and routes it through the right workflow. Simple, positive tickets get fast auto-replies (after human approval). Angry or complex tickets get escalated to a human agent immediately.

This blueprint maps out 12 nodes across frontend pages, backend APIs, AI prompts, databases, and human checkpoints. It's designed for a SaaS support team handling 50-500 tickets per day.

Architecture diagram

Support Inbox (Page) Ticket Dashboard (Page) Settings (Page)
↕
Tickets API Replies API Escalation API
↕
Sentiment Classifier (AI) Priority Scorer (AI) Auto-Reply Drafter (AI)
↕
Escalation Checkpoint Approval Gate
↕
Support DB (PostgreSQL) Notification Service

Node breakdown

Frontend pages

Backend APIs

AI prompts

Human checkpoints

Database & services

How it works end-to-end

  1. A customer submits a support ticket via email, contact form, or chat widget
  2. Tickets API ingests it and stores it in the Support DB
  3. Sentiment Classifier analyzes the ticket text and assigns a sentiment + confidence score
  4. Priority Scorer evaluates urgency based on sentiment, customer tier, and keywords
  5. If sentiment is negative with high confidence or priority is urgent: the Escalation Checkpoint routes the ticket to a human agent queue immediately
  6. Otherwise, the Auto-Reply Drafter generates a draft response
  7. The draft goes to the Approval Gate — a human reviews it in the Ticket Dashboard
  8. Once approved (or edited and approved), the reply is sent automatically
  9. The Support Inbox shows the full ticket history and reply status in real time

The two checkpoints are the key to this architecture. The Escalation Checkpoint prevents the AI from handling angry customers badly. The Approval Gate prevents any AI-generated reply from going out without human review. Together they make the automation safe.

Cursor export prompt

Open this blueprint in Bluemoonkey and export it, or copy the prompt below directly into Cursor's Agent mode:

Ready to paste 
Build this app exactly as described.
Start with database schema and auth, then APIs, then frontend pages.

## Support Inbox (Frontend Page)
Unified inbox showing all tickets with sentiment badges, priority labels,
and reply status. Filters by sentiment, priority, and date range.

## Ticket Dashboard (Frontend Page)
Detailed view of a single ticket: full message, customer history, AI
sentiment analysis, priority score, and drafted reply with editor.

## Settings (Frontend Page)
Configure auto-reply tone, escalation thresholds, team roles,
and notification preferences.

## Tickets API (REST Endpoint)
- GET /api/tickets — paginated list with sentiment + priority
- GET /api/tickets/:id — single ticket with full context
- POST /api/tickets/import — ingest from email or webhook

## Replies API (REST Endpoint)
- POST /api/replies — create draft reply for a ticket
- PATCH /api/replies/:id — approve/reject/edit draft
- POST /api/replies/:id/send — publish approved reply

## Escalation API (REST Endpoint)
- POST /api/escalate — route ticket to human agent queue
- GET /api/escalations — list escalated tickets with SLA timers

## Support DB (PostgreSQL)
- tickets: id, text, source, sentiment, sentiment_score, priority, status, created_at
- replies: id, ticket_id, draft_text, status, approved_by, sent_at, created_at
- users: id, email, name, role, tier, created_at
- escalations: id, ticket_id, assigned_to, sla_deadline, resolved_at, created_at

## Sentiment Classifier (AI Prompt)
Input: ticket text
Task: classify as positive, neutral, or negative with confidence
Output: sentiment label + confidence score (0-1)

## Priority Scorer (AI Prompt)
Input: ticket text, sentiment, customer tier
Task: assign priority level based on urgency and impact
Output: priority level (low/medium/high/urgent) + reasoning

## Auto-Reply Drafter (AI Prompt)
Input: ticket text, sentiment, priority, tone setting
Task: draft a helpful, professional reply
Output: draft reply text (max 500 characters)

## Escalation Checkpoint (Human Checkpoint)
If sentiment is negative with confidence above 0.8, or priority is
urgent, route the ticket to a human agent queue instead of auto-reply.
Send real-time notification to assigned agent.

## Approval Gate (Human Checkpoint)
Before any AI-drafted reply is sent to the customer, a human reviews
and approves it in the Ticket Dashboard. Approve, edit, or reject.
Rejected drafts can be regenerated with modified instructions.

For more prompt templates and tips, see our Cursor app prompt template guide. New to app architecture? Start with our guide on how to plan an app before coding.

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FAQ

What is an AI support triage system?

An AI support triage system automatically classifies incoming support tickets by sentiment and urgency, drafts replies, and routes complex cases to human agents. It uses AI for sentiment analysis and priority scoring, with human approval gates before any auto-reply is sent.

Can I export this blueprint to Cursor?

Yes. Open this blueprint in Bluemoonkey's free canvas and export it as a structured AI prompt formatted for Cursor's Agent mode. Paste it into Cursor, add "Build this app exactly as described," and it generates the full project.

How many nodes does this blueprint have?

This blueprint has 12 nodes: 3 frontend pages, 3 APIs, 3 AI prompts, 2 human checkpoints, and 1 database plus a notification service. Every node is editable in the free canvas.

Is the AI safe for customer-facing replies?

Yes — the architecture includes two checkpoints. The Escalation Checkpoint catches angry or urgent tickets before the AI touches them. The Approval Gate requires human review of every drafted reply before it's sent to the customer.