Support Triage & Resolution
Deflect L1 tickets and draft L2 responses using knowledge retrieval and evaluators. Use this page as a sample implementation pattern, then validate whether it belongs on your roadmap with an AI MRI.
How this pattern usually works.
Use this as a starting point. The AI MRI tells you whether this workflow belongs near the top of the backlog, what to fix manually first, and what needs to stay human.
- Classify & route tickets by intent and entitlement
- Retrieve product knowledge; draft responses
- Evaluator checks; publish or escalate
- Capture deltas to improve knowledge base
- Inputs: tickets, knowledge base, product contracts
- Outputs: resolved tickets, drafts, KB updates
- Entitlements and SLAs enforcement
- Safety filters to prevent overreach
- Continuously updated retrieval index
Before and after.
Typical improvements observed when this kind of workflow is implemented well. Your baseline determines exact gains.
| Metric | Before | After |
|---|---|---|
| Avg. first response time | 4.2 hours | < 5 minutes |
| L1 deflection rate | 8% | 42% |
| Tickets misrouted per week | 120 | 28 |
Frequently asked.
Direct answers to the questions we hear most from operators evaluating whether this workflow belongs on the roadmap.
The system uses intent classification models trained on your historical ticket data to categorize incoming requests by topic, urgency, and entitlement level. It considers customer tier, contract terms, and SLA requirements to route tickets to the right queue. Classification accuracy typically reaches 92-96% after a 2-3 week training period on your data.
No. When deployed correctly, AI deflection maintains or improves CSAT by 2-5 points. The key is answer quality: the system only auto-resolves tickets when confidence exceeds a configurable threshold (typically 85-90%). Low-confidence tickets are routed to human agents with a drafted response and relevant context, making human interactions faster and better-informed.
The system monitors resolved tickets for information not currently in the knowledge base and flags content gaps automatically. It also tracks which KB articles are used in resolutions and their success rates, surfacing articles that need updates. Human reviewers approve all KB changes before publication.
Deployment follows a three-phase ladder over 8-12 weeks. Shadow mode (weeks 1-3) runs the AI alongside human agents to benchmark accuracy. Assisted mode (weeks 4-7) presents AI drafts for agent approval. Autonomous mode (weeks 8+) handles qualifying tickets end-to-end with SLA monitoring and automatic escalation.
Yes. The underlying language models support 50+ languages natively. The system detects the customer language, retrieves relevant KB content, and generates responses in the same language. For organizations with multilingual support needs, this eliminates the constraint of routing tickets by agent language capability.
Book an AI MRI intro. We'll confirm the pain point, the signal sources, and whether this workflow deserves a build now, later, or not at all.