How to

April 10, 2026

Support data is fragmented across tools like Zendesk, Intercom, Freshdesk, and ServiceNow. Each system exposes tickets, customers, and conversations differently.

This use case shows how to:

• sync tickets in real time
• retrieve full customer context
• track conversations
• triage and update tickets
• respond with notes

All through a single normalized API.

Step 1: Listen for ticket events (real-time entry point)

Use webhooks where available to receive:

• ticket created
• ticket updated

This becomes your primary ingestion path.

Fallback:

• poll /ticket using updated_gte for integrations without webhook support

Step 2: List tickets (backfill + recovery)

const results = await sdk.ticketing.listTicketingTickets({
  connectionId,
  limit: 50,
  offset: 0,
  updated_gte: '2026-04-10T12:20:40.004Z',
  sort: 'updated_at',
  order: 'asc'
});

Use this for:

• initial sync
• missed webhook recovery
• periodic reconciliation

Step 3: Retrieve a ticket

const ticket = await sdk.ticketing.getTicketingTicket({
  connectionId,
  id: '1234'
});

Key fields:

• customer_id
• subject
• description
• status
• priority
• category_id
• tags
• user_id

This is your core support object.

Step 4: Retrieve customer context

const customer = await sdk.ticketing.getTicketingCustomer({
  connectionId,
  id: ticket.customer_id
});

Customer includes:

• name
• emails[]
• telephones[]
• tags[]

This allows:

• identifying the user
• grouping tickets by customer
• enriching support workflows

Step 5: Retrieve conversation history (notes)

const notes = await sdk.ticketing.listTicketingNotes({
  connectionId,
  ticket_id: ticket.id,
  limit: 50,
  sort: 'updated_at',
  order: 'asc'
});

Each note includes:

• description
• customer_id
• ticket_id
• user_id

Use this to:

• reconstruct conversation threads
• display agent replies
• power AI summarization or drafting

Step 6: Classify and route the ticket

First, retrieve available categories:

const categories = await sdk.ticketing.listTicketingCategories({
  connectionId,
  limit: 50
});

Then assign during triage:

await sdk.ticketing.updateTicketingTicket({
  connectionId,
  id: ticket.id,
  ticketingTicket: {
    priority: 'high',
    category_id: 'billing_issues',
    user_id: 'agent_123',
    tags: ['vip', 'refund']
  }
});

This step standardizes:

• routing
• ownership
• classification

Step 7: Respond to the ticket (create a note)

await sdk.ticketing.createTicketingNote({
  connectionId,
  ticketingNote: {
    ticket_id: ticket.id,
    customer_id: ticket.customer_id,
    description: 'We've identified the issue and are processing your refund.',
    user_id: 'agent_123'
  }
});

This works across all integrated platforms without adapting to:

• Zendesk comments
• Intercom replies
• Freshdesk notes

Step 8: Update ticket status

await sdk.ticketing.updateTicketingTicket({
  connectionId,
  id: ticket.id,
  ticketingTicket: {
    status: 'CLOSED',
    closed_at: new Date().toISOString()
  }
});

Status is normalized:

• ACTIVE
• CLOSED

Step 9: Keep data in sync

Tickets

• Webhooks for real-time updates
• Poll with updated_gte as fallback

Notes

• Webhooks where supported
• Poll /note with updated_gte if needed

await sdk.ticketing.listTicketingNotes({
  connectionId,
  updated_gte: lastSyncTime
});

Customers

• Poll-based sync for consistency
• Webhooks when available

Categories

• Poll periodically (low change frequency)

Final Architecture

This flow gives you:

• real-time ticket ingestion (webhooks)
• unified ticket + customer model
• full conversation reconstruction
• consistent triage and routing
• cross-platform response handling
• reliable fallback via polling

All without writing:

• per-platform ticket logic
• per-platform comment handling
• per-platform customer retrieval

What this enables

• unified support dashboards
• AI support agents operating across tools
• automated ticket routing systems
• cross-platform analytics and reporting
• shared inbox experiences across multiple support systems

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