Automated Customer Support Ticket Routing & AI Resolution

As of v1.0.5, the platform includes a fully automated customer support pipeline. Inbound support tickets are ingested via webhook, classified and routed by a rules-based routing engine, and resolved autonomously by a dedicated AI resolution agent — with no human support team involved.

Architecture Overview

Inbound Request (email / in-app / integration)
        │
        ▼
  Ingestion Webhook
  (validate · normalize · deduplicate)
        │
        ▼
  Ticket Store (Postgres)
        │
        ▼
  Routing Engine
  (classify · score · dispatch)
        │
        ▼
  AI Resolution Agent
  (resolve · respond · log confidence)
        │
        ▼
  Customer Reply (originating channel)

Support Ticket Schema

Each ticket persists the following fields:

Field	Type	Description
`id`	`uuid`	Unique ticket identifier
`customer_id`	`uuid`	Associated customer account
`channel`	`enum`	Source channel (`email`, `in_app`, `webhook`)
`subject`	`text`	Ticket subject line
`body`	`text`	Full ticket body
`category`	`enum`	Classified topic (`billing`, `technical`, `onboarding`, `general`)
`priority`	`enum`	`low`, `normal`, `high`, `urgent`
`status`	`enum`	`open`, `in_progress`, `pending_customer`, `resolved`, `closed`
`assigned_agent`	`text`	AI agent handling resolution
`resolution`	`text`	Final resolution text sent to customer
`confidence_score`	`float`	AI resolution confidence (0–1)
`created_at`	`timestamp`	Ticket creation time
`updated_at`	`timestamp`	Last status change time

Ticket Ingestion Webhook

The ingestion webhook is the entry point for all inbound support requests.

Endpoint

POST /api/webhooks/support/ingest

Behaviour

Accepts payloads from email-to-webhook bridges, in-app contact forms, and third-party integrations.
Validates and normalizes the payload schema before writing to the ticket store.
Implements idempotency via a caller-supplied external_id field — duplicate submissions with the same external_id are discarded.

Example Payload

{
  "external_id": "email-msg-abc123",
  "channel": "email",
  "customer_email": "user@example.com",
  "subject": "Cannot access my dashboard",
  "body": "I've been unable to log in since yesterday. I get a 403 error."
}

Response

{
  "ticket_id": "t_01j...",
  "status": "open",
  "created_at": "2025-01-15T10:23:00Z"
}

Routing Engine

Once a ticket is created, the routing engine classifies and dispatches it.

Classification

The engine assigns each ticket a category and priority based on:

Keyword and semantic analysis of the subject and body.
Customer account context (e.g. active subscription tier, recent billing events).
Historical ticket patterns for the customer.

Categories:

Category	Examples
`billing`	Invoice questions, charge disputes, subscription changes
`technical`	Login issues, API errors, performance problems
`onboarding`	Setup help, feature questions during trial
`general`	Everything else

Priority Scoring

Priority	Criteria
`urgent`	Service outage, data loss, security concern
`high`	Core feature broken, paying customer blocked
`normal`	Degraded experience, workaround available
`low`	General enquiry, feature request

Dispatch

After classification, the ticket is dispatched to the AI Resolution Agent. The routing engine records the assigned agent on the ticket record and transitions status to in_progress.

AI Resolution Agent

The AI Resolution Agent attempts autonomous end-to-end resolution for every routed ticket.

Resolution Process

Context loading — Pulls customer account history, usage data, and any prior tickets for the same customer.
Resolution generation — Produces a response using the ticket content and loaded context.
Confidence scoring — Assigns a confidence_score between 0 and 1 to the generated resolution.
Delivery — Sends the response back to the customer via the originating channel and transitions ticket status to resolved.
Escalation — If confidence_score falls below the configured threshold, the ticket is flagged and status is set to pending_customer pending a follow-up cycle.

Confidence Thresholds

Score Range	Action
`0.85 – 1.00`	Auto-resolve and send response
`0.60 – 0.84`	Send response, flag for review
`0.00 – 0.59`	Do not send; escalate for retry or manual review

Logging

Every resolution attempt is logged with the full context snapshot, generated resolution text, and confidence score for auditability.

Ticket Lifecycle

open
  │
  └─► in_progress  (routing engine dispatches to AI agent)
          │
          ├─► resolved       (AI confidence ≥ 0.85, response sent)
          │       └─► closed (no further activity)
          │
          └─► pending_customer  (low confidence, flagged)
                  └─► in_progress  (re-queued for next resolution cycle)

Relationship to Other Features

Customer Onboarding — The onboarding ticket category surfaces common setup blockers that the AI agent resolves using onboarding flow context.
Email Notifications — Ticket responses are delivered through the same notification infrastructure used for pipeline event emails.
Customer Health Scoring — Open and unresolved ticket counts feed into the customer health score model.