Use Case

Search Indexing & Product Tagging

Customer Support Ticket Classification (Help Desk & ITSM)

Customer support and IT help desk systems continuously ingest large volumes of incoming tickets across email, chat, and web forms. Each ticket must be classified into the correct category, department, or routing queue in real time.

Problem: Every ticket is routed through full classification models, creating high inference cost at scale (Zendesk-style workloads).
With moco: Simple, high-confidence tickets are routed through lightweight decision paths, while ambiguous cases use full model inference.
Impact: 25–45% reduction in classification compute cost with faster routing latency and improved operational efficiency.

Ticket Prioritization for SLA Management

Support teams must prioritize incoming tickets based on urgency, customer tier, and business impact to meet SLA requirements and prevent escalation. This requires real-time scoring of incoming requests across multiple signals.

Problem: All tickets are evaluated with full priority models, even when priority is obvious from simple heuristics (e.g., VIP users or outage keywords).
With moco: High-confidence priority decisions are handled with low-cost routing logic, reserving full inference for edge cases requiring deeper analysis.
Impact: Reduced GPU/CPU load by 20–40% while maintaining SLA accuracy and improving response time consistency.

Sentiment & Intent Analysis for Support Automation

Support platforms analyze customer messages to detect sentiment and intent, enabling automated replies, escalation, or agent routing. This is critical for chat-based systems and large-scale customer success operations.

Problem: Full transformer-based models are applied to every message, even when sentiment or intent is already obvious from structure or keywords.
With moco: moco filters easy cases (e.g., clearly negative or clearly informational messages) through lightweight paths and escalates uncertain cases to full models.
Impact: 30–50% reduction in inference cost with faster response times and improved scalability for real-time support systems.