Design AI Support Ticket Prioritization

Product Context

AcmeCloud is a B2B SaaS platform with a large customer support operation. You are designing an AI system that reads incoming support tickets and recommends priority, routing, and likely resolution suggestions so agents can respond faster and high-severity issues are handled first.

Scale

Task

1. Clarify the product goal and define the prediction targets: priority, team routing, and recommended resolution candidates.
2. Design an end-to-end ML system, including data pipelines, feature computation, model training, and online serving.
3. Propose a multi-stage architecture for retrieval, ranking, and optional re-ranking of suggested resolutions or macros.
4. Explain how you would handle cold-start tickets, sparse customer history, multilingual text, and rapidly changing incident patterns.
5. Define offline and online evaluation, including business metrics, guardrails, and rollout strategy.
6. Identify key failure modes such as feature drift, training-serving skew, stale knowledge content, and misrouting of urgent tickets.

Constraints

• High-severity enterprise outage tickets must be prioritized with very high recall.
• Some ticket metadata arrives late or is incomplete at creation time.
• The system must support 20 languages, but only 6 have abundant labeled data.
• PII in ticket text cannot be exposed to downstream analytics without redaction.
• Serving cost matters: the default path should run on CPU, with limited GPU use for heavier models or batch jobs.