Implementation of AI-Based Support Ticket Routing
Ticket Routing automatically assigns incoming requests to the right team or specific specialist. Correct routing reduces first response time and increases FCR (First Contact Resolution).
Routing Factors
Smart routing considers multiple dimensions simultaneously:
- Topic: technical issue, billing, complaint, information request
- Priority: critical (service unavailable) vs regular
- Complexity: basic (closed with template response) vs complex (requires investigation)
- Customer segment: VIP, corporate, mass market — different SLAs
- Language: routing to Russian-speaking/English-speaking operator
- Customer history: repeat request on same topic → to specialist already familiar with history
Ticket Routing System Architecture
class TicketRoutingDecision(BaseModel):
team: str # billing, tech_support, sales, escalation
priority: Literal["P1", "P2", "P3", "P4"]
assignee_id: str | None # specific agent or None for auto-assignment
reasoning: str # explanation of decision
suggested_response_template: str | None
def route_ticket(ticket: Ticket) -> TicketRoutingDecision:
context = build_context(ticket) # customer history, current team load
return llm_classify(ticket.text, context)
Load Balancing
Routing must account for current agent load. Algorithm: primary classification by competencies → selection of least loaded agent with required competency. Load data: open tickets per agent, average processing time.
Integration with Helpdesk Systems
- Zendesk: Triggers API for automatic tagging and assignment
- Freshdesk: Webhooks + API for ticket update
- Jira Service Management: REST API, automatic rules
- ITSM systems: ServiceNow, OTRS — via REST API
Performance Metrics
Before/after AI routing implementation:
- Routing accuracy: % of requests routed to correct team without reassignment. Target: > 90%
- First response time: reduction through immediate assignment
- Misrouting rate: % of tickets reassigned after initial routing