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.
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
Node breakdown
Frontend pages
- Support Inbox — unified inbox showing all incoming tickets with sentiment badges, priority labels, and reply status. Filters by sentiment, priority, and date.
- Ticket Dashboard — detailed view of a single ticket: full message, customer history, AI sentiment analysis, priority score, and drafted reply with an editor for human review.
- Settings — configure auto-reply tone, escalation thresholds (e.g. escalate all negative-sentiment tickets above 0.8 confidence), team member roles, and notification preferences.
Backend APIs
- Tickets API —
GET /api/tickets(paginated list with sentiment + priority),GET /api/tickets/:id(single ticket with full context),POST /api/tickets/import(ingest from email/webhook). - Replies API —
POST /api/replies(create draft),PATCH /api/replies/:id(approve/reject/edit),POST /api/replies/:id/send(publish approved reply). - Escalation API —
POST /api/escalate(route ticket to human agent queue),GET /api/escalations(list escalated tickets with SLA timers).
AI prompts
- Sentiment Classifier — input: ticket text; task: classify as positive, neutral, or negative with confidence score; output: sentiment label + confidence (0-1).
- Priority Scorer — input: ticket text + sentiment + customer tier; task: assign priority (low, medium, high, urgent); output: priority level + reasoning.
- Auto-Reply Drafter — input: ticket text + sentiment + priority + tone setting; task: draft a helpful, professional reply; output: draft reply text (max 500 characters).
Human checkpoints
- Escalation Checkpoint — if sentiment is negative and confidence is above threshold (e.g. 0.8), or priority is urgent, the ticket is automatically routed to a human agent queue instead of auto-reply.
- Approval Gate — before any auto-generated reply is sent to the customer, a human reviews and approves it. The drafted reply appears in the Ticket Dashboard with approve, edit, and reject buttons.
Database & services
- Support DB (PostgreSQL) — tables: tickets (id, text, source, sentiment, priority, status, created_at), replies (id, ticket_id, draft_text, status, approved_by, sent_at), users (id, email, name, role, tier), escalations (id, ticket_id, assigned_to, sla_deadline, resolved_at).
- Notification Service — sends real-time notifications to agents when tickets are escalated, when drafts need approval, and when SLA timers are at risk.
How it works end-to-end
- A customer submits a support ticket via email, contact form, or chat widget
- Tickets API ingests it and stores it in the Support DB
- Sentiment Classifier analyzes the ticket text and assigns a sentiment + confidence score
- Priority Scorer evaluates urgency based on sentiment, customer tier, and keywords
- If sentiment is negative with high confidence or priority is urgent: the Escalation Checkpoint routes the ticket to a human agent queue immediately
- Otherwise, the Auto-Reply Drafter generates a draft response
- The draft goes to the Approval Gate — a human reviews it in the Ticket Dashboard
- Once approved (or edited and approved), the reply is sent automatically
- 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.
Bluemoonkey input prompt
This is what you paste into Bluemoonkey's canvas — a plain-language description of what the app should do. Bluemoonkey's AI advisor reads this and generates the full node architecture (pages, APIs, database, AI prompts, and checkpoints) shown above. You don't need to write anything technical here — just describe what you want in your own words.
A customer support app that automatically sorts incoming tickets so my team isn't overwhelmed.
I need a Support Inbox showing all tickets with a sentiment label, priority level, and reply status, with filters by sentiment, priority, and date.
A Ticket Dashboard showing one ticket in detail — the full message, customer history, sentiment and priority the AI assigned, and a drafted reply I can edit.
A Settings page to configure the tone of auto-replies, set thresholds for when a ticket should be escalated to a human, manage team roles, and set notification preferences.
The AI should read every incoming ticket, figure out if it's positive, neutral, or negative, assign a priority level, and draft a helpful reply.
If a ticket is very negative or urgent, it should skip the AI reply and go straight to a human agent's queue with a notification.
For everything else, a human must still review and approve the AI's drafted reply before it's sent to the customer — nothing goes out automatically.After Bluemoonkey generates the blueprint, review each node — adjust wording, check the escalation thresholds, and confirm the architecture matches what you need. From there, Bluemoonkey's canvas gives you a build-ready prompt for Cursor or Claude Code with one click — no manual prompt writing required.
New to app architecture? Start with our guide on how to plan an app before coding.
Fork this blueprint
Open the free canvas, import this architecture, and customize it for your support workflow.
Open Canvas — Free →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. Paste the Bluemoonkey input prompt above into the free canvas, let the AI advisor build out the blueprint, then click Export. Bluemoonkey generates a structured, Cursor-ready prompt for you automatically — no manual prompt writing needed.
Do I need to write the Cursor prompt myself?
No. You only write the short, plain-language prompt above and paste it into Bluemoonkey's canvas. Bluemoonkey's AI advisor turns it into the full node architecture, and when you click Export, it generates the structured, technical prompt for Cursor or Claude Code automatically.
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.