Customer support is one of the highest-impact applications of generative AI. For a SaaS client handling 15,000 support tickets per month, we built an LLM-powered assistant that now resolves 70% of tier-1 tickets without human intervention — with a customer satisfaction score that matches their human agents.
Architecture
The system uses a retrieval-augmented generation (RAG) approach. When a customer submits a ticket, the system embeds the query, searches a vector database of knowledge base articles and past resolutions, and generates a contextually relevant response using a fine-tuned language model.
Guardrails and Safety
- Confidence scoring — tickets below a confidence threshold are escalated to human agents
- Topic classification — sensitive topics like billing disputes are always routed to humans
- Response validation — generated responses are checked against a set of policy rules before being sent
- Feedback loop — agent corrections are used to continuously improve the model
The key insight is that generative AI for support is not about replacing humans — it is about handling the repetitive, well-documented queries so that human agents can focus on complex, high-value interactions. The result is better outcomes for customers and more fulfilling work for the support team.