Email & Ticket Triage
Automatically classify, prioritize and route incoming emails and tickets to the right team.
Support teams lose time manually screening and forwarding incoming requests. An AI model detects topic, urgency and the right contact, and suggests a reply. Critical cases are reliably flagged and reach the right person immediately.
- Faster response times for critical requests.
- More even workload across support teams.
- Fewer manual forwards.
Handling time per request
Time to value
Correct routing in testing
Escalations from delayed replies
Derived from average screening time per ticket and daily ticket volume in the support team.
- 01
Category analysis
Define typical request types and responsibilities.
- 02
Model training
Calibrate classification using historical tickets.
- 03
System integration
Integrate with Zendesk/Jira and email inboxes.
- 04
Escalation rules
Set guardrails for urgent and sensitive cases.
- 05
Operation & tuning
Ongoing evaluation of accuracy and refinement.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Zendesk/Jira
Ticket history and past routing decisions.
Email inboxes
Incoming customer requests as raw data.
Team responsibilities
Mapping rules between topics and teams.
SLA definitions
Deadlines and priority levels per request type.
CRM customer data
Context on customer value and history.
Evaluation sets
Vetted examples for classification quality assurance.
Support team
Less time spent on screening and routing.
Customer service lead
Better control over capacity and priority.
Customers
Faster and more relevant responses.
Quality management
Measurable SLA compliance.
Faster response times for critical requests.
More even workload across support teams.
Fewer manual forwards.
Better SLA compliance.
More time for complex cases instead of sorting.
With a solid data foundation this use case gets faster, cheaper and far more stable.
A central ticket history enables robust training.
Unified SLA data ensure correct prioritization.
API connections to existing systems reduce integration effort.
Monitoring infrastructure allows continuous quality control.
Service & repair assistant
Technical fault reports are pre-qualified directly.
Internal enterprise GPT
Identified knowledge gaps improve the internal knowledge base.
Business intelligence
Ticket data provide metrics on topic distribution.
Internal Enterprise GPT
A company-owned chat assistant that reliably answers questions on processes, policies and expertise.
Document Chat (RAG)
Search large document sets via chat instead of manually digging through contracts and reports.
Service & Repair Assistant
Technicians get precise repair guidance on-site, drawn from manuals and ticket history.