Goal: No-code Zapier + ChatGPT workflow that labels/prioritizes tickets, drafts replies, and requires agent approval.

Recommendation
Start with a single Zap per incoming channel (email, Zendesk, Intercom, Slack). Use Zapier to trigger on a new ticket, call ChatGPT to (1) classify & prioritize, (2) draft a reply, then create a draft/ticket comment or Slack message for agent approval. Route high-priority or sensitive tickets to human-first paths.

Step-by-step flow (no-code)
1) Trigger: New ticket in your source (Zendesk/New Email/Intercom) — capture subject, body, attachments, requester.
2) Optional Filter: Quick triage via Zapier Filters to discard auto-replies or known spam.
3) Action — Classification: Use Zapier’s OpenAI/ChatGPT action. Prompt to return JSON: {category, subcategory, priority (P1/P2/P3), sentiment, recommended SLA minutes}. Keep token usage low by asking for short outputs.
4) Action — Update ticket fields: Write the classification into your ticket system via Zapier (custom fields: category, priority, confidence).
5) Action — Draft reply: Call ChatGPT again with a short system prompt that injects ticket metadata + selected reply tone + length + any company support KB links. Ask ChatGPT to return: {reply_body, suggested_macro_id}.
6) Action — Create approval task: Post the drafted reply to Slack or create an agent task in your ticketing tool with the draft and two buttons (Approve/Request Edits). Use Zapier’s Slack message + interactive buttons or create a Zendesk internal note with status=“Draft”.
7) Path on Approve: If agent approves (button click or ticket update), send final update via Zapier to post the reply, add tags, and close or set status.
8) Path on Edit: If agent requests edits, send reply back to ChatGPT with agent comments to produce a revised draft (loop until approval).
9) Escalation: Use Zapier Paths to auto-escalate P1s to paging/phone and add created_at to SLA tracking.

Decision criteria
- Budget: Zapier multi-step + OpenAI calls require a paid Zapier plan and API spend for ChatGPT. If budget is tight, batch lower-priority tickets for nightly processing.
- Skill: No-code but requires prompt design and testing. Small tweaks in prompts greatly affect output quality.
- Team size & SLA: For small teams, keep human-in-loop mandatory. For large teams with high trust and strict SLAs, add auto-send for P3s.
- Compliance/privacy: Don’t forward PII to third-party models unless contract and data controls permit.

Best-for / Avoid-if
- Best for: teams wanting fast no-code setup, many integrations, and human review before reply.
- Avoid if: you require on-premise models, strict data residency, or extremely complex multi-turn workflows that need deep context.

Practical checklist before go-live
- Create ticket custom fields for category, priority, ai_confidence.
- Build test tickets covering edge cases (refund, legal, abusive language).
- Prepare 4–6 short classification and reply prompts (examples below).
- Configure Slack/Zendesk actions for approvals and buttons.
- Set rate limits and monitoring for OpenAI errors; log outputs for QA.

Example prompts (short)
Classification prompt: "Return JSON: {category,priority,confidence}. Classify this ticket: [ticket text]. Categories: billing, technical, account, feedback, legal. Priority rules: P1=system down/PII breach, P2=customer impact, P3=general."
Draft prompt: "Write a concise agent reply (100–150 words) using polite, professional tone. Include steps to reproduce if needed and a next-step CTA. Attach KB link: [kb_url]."

Start small (single channel, P3s only), iterate prompts, then expand. Good luck.

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