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Faster first-response time
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Tickets fully automated
The Challenge
Support teams were spending most of their time on repetitive, low-complexity tickets, slowing down response times on higher-priority issues.
The Approach
Designed an LLM-backed copilot that classifies incoming tickets, drafts context-aware responses from a knowledge base, and automatically resolves common requests end to end.
Under the Hood
Retrieval-augmented generation pipeline with a vector database for knowledge retrieval, a Node.js orchestration layer, and a queue-based automation engine for multi-step workflows.
Key Features
- Automated ticket classification and prioritization
- Context-aware AI-drafted responses
- End-to-end automation for common request types
- Human-in-the-loop review for low-confidence responses
Challenges
- Keeping the knowledge base fresh without manual re-indexing
- Balancing automation confidence thresholds to avoid incorrect auto-resolutions
- Streaming AI responses in real time without blocking the support workflow