AI Support Agents
Use approved knowledge to draft routine answers, collect missing context, route tickets, and escalate uncertainty to a support specialist.
The Problem
- • Support tickets growing faster than your team
- • Repeated questions consume specialist time
- • Important context is scattered across tools
- • Urgent or sensitive tickets are not consistently identified
- • Quality varies between shifts and support channels
The Solution
- ✓ Retrieve answers from an approved knowledge set
- ✓ Draft a response with source context for review
- ✓ Apply routing and priority rules consistently
- ✓ Escalate policy-sensitive and low-confidence cases
- ✓ Record the workflow outcome for quality review
Pilot Metrics
Capture a baseline first, then compare the pilot against the same queue, ticket types, and quality standard.
Quality
Reviewed answer accuracy and policy compliance
Speed
Time to first useful response and resolution
Operations
Escalation precision, rework, and cost per ticket
Suggested Pilot Scope
- • One support queue and one clearly owned knowledge base
- • A limited set of routine, low-risk ticket categories
- • Draft-only mode before autonomous customer responses
- • Named reviewers and a documented escalation path
Inputs We Need
- • Sample tickets with sensitive information removed
- • Current policies, macros, help articles, and routing rules
- • Ticket categories that always require human handling
- • Baseline quality, response-time, and volume measures