# Customer Support Triage Workflow — AI Workflow export
# https://invitationbuddy.com/workflows/customer-support-triage-workflow
schema_version: "1.0"
exported_at: "2026-08-16T10:16:15+00:00"
source: "https://invitationbuddy.com/workflows/customer-support-triage-workflow"
workflow:
  name: Customer Support Triage Workflow
  slug: "customer-support-triage-workflow"
  version: "1.0.0"
  tagline: "Classify, prioritise, summarise and route every incoming ticket"
  summary: "Reads each new ticket, classifies it, detects sentiment and urgency, summarises the thread and routes it to the right queue with a suggested reply."
  description: |-
    Support queues fail at triage, not at answering. This workflow reads every incoming ticket, works out what it is actually about, how urgent it is and who should own it, then hands the agent a summary and a suggested reply.
    
    The agent still sends. What changes is that they open a ticket already understood.
  category: Customer Support
  type: Automation
  difficulty: beginner
  automation_level: mostly_automated
  pricing: freemium
  language: en
  trigger:
    type: email_event
    detail: New ticket created
  input_types:
    - email
    - text
    - api
  output_types:
    - summary
    - email
  author: null
  company: null
  last_updated: "2026-07-09"
overview:
  purpose: |-
    Support queues fail at triage, not at answering. This workflow reads every incoming ticket, works out what it is actually about, how urgent it is and who should own it, then hands the agent a summary and a suggested reply.
    
    The agent still sends. What changes is that they open a ticket already understood.
  business_value: "Cuts first-response time and stops urgent tickets sitting behind routine ones."
  expected_outcome: "Every ticket categorised, prioritised, summarised and routed within seconds, with a draft reply attached."
  problems_solved:
    - Urgent tickets queued behind trivial ones
    - "Agents re-reading long threads to find the actual question"
    - Inconsistent categorisation makes reporting useless
    - "Non-English tickets wait for a specific agent"
  ideal_users:
    - Support teams with meaningful ticket volume
    - "E-commerce operations handling order queries"
    - SaaS teams working from a shared inbox
  benefits:
    - "Urgency detected from content, not from the customer ticking a box"
    - Agents open tickets already summarised
    - Consistent categories make reporting trustworthy
    - "Non-English tickets triaged immediately"
  limitations:
    - Suggested replies need agent review before sending
    - Sarcasm and understatement fool sentiment detection
    - The category taxonomy needs maintaining as the product changes
    - Does not resolve tickets — it prepares them
  success_metrics:
    - Median first response time
    - Routing accuracy (tickets reassigned)
    - Agent handling time per ticket
    - "CSAT versus the pre-automation baseline"
metrics:
  estimated_minutes: 3
  estimated_cost: 0.09
  cost_currency: USD
  estimated_tokens: 4500
  success_rate: 91
  automation_percent: 88
  human_review_percent: 12
industries:
  - SaaS
  - "E-commerce"
  - Retail
  - Travel
tags:
  - Quick Win
  - "Real-time"
  - Multilingual
  - Beginner Friendly
ai_skills:
  - Summarization
  - Reasoning
  - Translation
tools:
  -
    name: ChatGPT
    role: primary
    required: true
    url: "https://invitationbuddy.com/ai-directory/chatgpt"
  -
    name: Claude
    role: supporting
    required: true
    url: "https://invitationbuddy.com/ai-directory/claude"
models: []
integrations:
  -
    name: OpenAI
    kind: ai_provider
    auth_type: api_key
    required: true
  -
    name: Slack
    kind: communication
    auth_type: oauth
    required: true
  -
    name: Webhook
    kind: protocol
    auth_type: webhook
    required: false
  -
    name: Zapier
    kind: automation
    auth_type: oauth
    required: false
prerequisites:
  -
    kind: api_key
    label: LLM API key
    detail: For classification and drafting.
    required: true
  -
    kind: account
    label: Helpdesk with webhooks
    detail: "Zendesk, Intercom, Freshdesk or a shared inbox with automation."
    required: true
  -
    kind: software
    label: Category taxonomy
    detail: A documented list of categories and their owning queues.
    required: true
steps:
  -
    number: 1
    title: Ticket created trigger
    description: "Webhook fires with the ticket body, customer record and any thread history."
    input: Ticket payload
    output: Job
    estimated_minutes: 1
    difficulty: beginner
    automation: auto
    manual_review: false
  -
    number: 2
    title: Classify and score urgency
    description: "Assign a category, detect sentiment, and score urgency from the content rather than the customer's self-selection."
    model: "GPT-4o"
    prompt: |-
      Classify this support ticket.
      
      Ticket:
      {{ticket_body}}
      
      Available categories: {{categories}}
      
      Return JSON:
      {
        "category": "one of the above",
        "urgency": "low|normal|high|critical",
        "sentiment": "positive|neutral|frustrated|angry",
        "language": "ISO code",
        "is_churn_risk": true|false,
        "one_line_summary": "what they actually need"
      }
      
      Judge urgency from the impact described, not from tone. A calm message about total data loss is critical.
    input: Ticket body
    output: Category + urgency + sentiment
    estimated_minutes: 1
    difficulty: beginner
    automation: auto
    manual_review: false
    tips:
      - "Tell it to judge urgency by impact, not tone — otherwise politeness gets deprioritised"
  -
    number: 3
    title: Summarise the thread
    description: "For tickets with history, condense the whole thread to what has been tried and what is still outstanding."
    model: "GPT-4o"
    input: Thread history
    output: Thread summary
    estimated_minutes: 1
    difficulty: beginner
    automation: auto
    manual_review: false
  -
    number: 4
    title: Draft a suggested reply
    description: "Produce a first-draft reply grounded in the help centre, in the customer's language."
    model: Claude Sonnet
    input: Ticket + KB articles
    output: Draft reply
    estimated_minutes: 1
    difficulty: beginner
    automation: auto
    manual_review: false
    warnings:
      - The draft may confidently state a policy that does not exist — agents must verify before sending
  -
    number: 5
    title: Route and notify
    description: "Assign to the owning queue, set priority, and alert the on-call channel for anything critical."
    tool: Slack
    input: Classified ticket
    output: Routed ticket
    expected_result: Critical tickets surfaced immediately; everything else in the right queue.
    estimated_minutes: 1
    difficulty: beginner
    automation: auto
    manual_review: false
  -
    number: 6
    title: Agent review and send
    description: "The agent reads the summary, checks the draft against policy, edits and sends."
    input: Ticket + summary + draft
    output: Sent reply
    estimated_minutes: 3
    difficulty: beginner
    automation: manual
    manual_review: true
prompts:
  -
    title: Classify and score urgency
    kind: user
    body: |-
      Classify this support ticket.
      
      Ticket:
      {{ticket_body}}
      
      Available categories: {{categories}}
      
      Return JSON:
      {
        "category": "one of the above",
        "urgency": "low|normal|high|critical",
        "sentiment": "positive|neutral|frustrated|angry",
        "language": "ISO code",
        "is_churn_risk": true|false,
        "one_line_summary": "what they actually need"
      }
      
      Judge urgency from the impact described, not from tone. A calm message about total data loss is critical.
    variables:
      - ticket_body
      - categories
    model_hint: "GPT-4o"
use_cases:
  -
    title: Shared inbox triage
    description: "Turn an unstructured shared inbox into a prioritised, categorised queue."
    industry: SaaS
    outcome: Nothing urgent gets buried
  -
    title: Order and delivery queries
    description: "Auto-classify and route high-volume order questions."
    industry: "E-commerce"
    outcome: Faster routine resolution
  -
    title: Multilingual support
    description: Triage every language immediately rather than waiting for a specific agent.
    industry: Travel
    outcome: "Same-day response in any language"
faq:
  -
    question: "Can this reply to customers automatically?"
    answer: "It can, and for a narrow set of well-understood queries that is reasonable. Start with agent review on everything, measure where the drafts are consistently right, and only then automate that slice."
  -
    question: "How do I stop it misjudging urgency?"
    answer: "Instruct it to score on described impact rather than tone, then sample a week of classifications against what agents actually escalated."
  -
    question: "What about languages we do not support?"
    answer: "It will triage and summarise them regardless, which at minimum tells you what has arrived and how urgent it is."
