Customer Support

Support teams use Amodal to gather context before responding, route tickets intelligently, and accelerate resolution by pulling relevant information from internal systems automatically.

Context Gathering

User: "Customer reports they can't complete checkout — ticket #4521"
 
Agent:
  → Pulls ticket details from Zendesk
  → Looks up customer account in the CRM
  → Checks for active incidents affecting checkout
  → Queries recent error logs for the customer's session
  → Checks if the customer's payment method has known issues
 
  → Presents: "Customer is on iOS 17.2 Safari, which has a known bug
     with our payment form (KB: known-issues/safari-17.2). The fix
     shipped in v2.3.1 but their CDN is still serving v2.3.0."

Intelligent Routing

Set up automations to classify and route tickets:

await client.automations.create({
  name: 'Ticket Router',
  prompt: `Classify the incoming ticket by:
    1. Category (billing, technical, account, feature-request)
    2. Severity (critical, high, medium, low)
    3. Required expertise (frontend, backend, payments, security)
    Route to the appropriate team queue.`,
  trigger: {
    source: 'webhook',
    filter: { 'event.type': 'ticket.created' },
  },
  output: { channel: 'webhook', target: 'https://api.zendesk.com/routing' },
})

Key Connections

System	What It Provides
Zendesk / Intercom	Ticket details, customer history
CRM (Salesforce, HubSpot)	Account info, subscription status
Internal APIs	Product data, feature flags, config
Monitoring (Datadog)	Error logs, performance data

KB Growth

Support agents contribute significantly to the learning flywheel. Every resolved ticket can produce:

Known issues: bugs and workarounds
Resolution patterns: common fixes for recurring problems
False positives: alerts that look like customer issues but aren't