Workflow: triage Zendesk tickets to ChatGPT via Zapier

Recommendation (short): Use a Zapier Zap triggered by “New Ticket” in Zendesk → fetch ticket + recent comments → call ChatGPT to produce a concise summary, priority label, and confidence score → write tags and an internal note back to Zendesk. Add a human-review path when confidence is low.

Step-by-step Zapier flow
1) Trigger: Zendesk — New Ticket (or New Ticket: Matching Filter for only inbound channels you care about).
2) Action: Zendesk — Find Ticket (or Get Ticket) to pull full ticket fields. Also fetch associated recent comments (use the “List Comments” API if available in the Zap step). Include requester name, requester history summary field (if you maintain one), and attachments metadata.
3) Formatter (optional): If comments are long, use Formatter to trim to the most recent N comments or to concatenate: requester message + last 2 agent comments.
4) Action: Webhooks by Zapier — POST to ChatGPT (OpenAI) or to the ChatGPT action (if available). Payload should include:
- System instruction: role and what to produce (one-sentence summary, recommended priority: P1/P2/P3, rationale, short suggested internal note, confidence score 0–1).
- Ticket fields: subject, body, last 3 comments, customer tier, SLA, relevant tags, attachment URLs (if needed).
- A maximum token limit and an instruction to be concise.
5) Filter/Path: Parse ChatGPT’s JSON response. If confidence >= threshold (recommended 0.7), follow Auto-apply path; else follow Human-review path.
6a) Auto-apply: Zendesk — Update Ticket: add priority tag, update Priority field, append internal note with ChatGPT summary and rationale, and optionally notify assignee.
6b) Human-review: Zendesk — Add tag like needs-ai-review AND create a ticket in a review view or send a Slack/Email to triage team with the suggested summary + a link to ticket.
7) Logging: store the raw model output in a datastore (Google Sheet / Airtable) for audit, quality tracking, and prompt tuning.

Preserving context — best practices
- Always send the ticket body + last 2–3 comments, requester tier, SLA, and previous tags. If >4096 tokens, truncate older agent comments first.
- Include attachment metadata and offer a human flag for attachments needing review (images, logs).
- Keep a versioned “prompt template” saved in Git/Docs for reproducibility.

Fallback when low-confidence
- Mark ticket with needs-ai-review tag and route to a human queue.
- Optionally escalate to a senior agent or create a Slack message with quick actions (Approve / Reject / Edit suggested priority).
- Log the low-confidence cases for prompt and model improvement.

Decision criteria (when to auto-apply vs human):
- Auto-apply if confidence >= 0.7, customer tier != enterprise escalation, ticket open < 12 hours, no attachments that imply technical depth.
- Human-review if low confidence, enterprise customer, billing or legal keywords, or attachments present.

Checklist (practical)
- [ ] Trigger filters set to only relevant channels
- [ ] Fetch full comments + requester metadata
- [ ] Prompt template with required outputs + confidence number
- [ ] Confidence threshold and Zap path configured
- [ ] Writeback to Zendesk (tags + internal note)
- [ ] Audit storage for model response
- [ ] Slack/email human-review path

Best-for: teams wanting faster triage, small/mid teams that need automation but still keep human-in-loop. Avoid if: you have strict compliance requiring human-only decisions or very sensitive PII.

Depends on: budget (API costs/token usage), Zapier plan & task limits, team size (who reviews low-confidence), and how strict your SLAs are.

If you want, I can draft the exact ChatGPT prompt and Webhooks payload next.

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