AI Customer Support Assistant

The Challenge

A growing support volume was overwhelming the team and slowing response times.

Our Solution

We integrated a large-language-model assistant trained on the client knowledge base, with sentiment analysis and seamless human hand-off.

How we approach this kind of build

The accuracy of a support assistant comes almost entirely from retrieval, not from the model. Answers are grounded in the client's own help articles, policies, and past resolved tickets, and the assistant cites which document it used. When retrieval returns nothing relevant, the correct behaviour is to say so and hand off — not to generate a plausible-sounding answer from general knowledge.

Escalation is designed as a first-class path rather than a failure case. The assistant hands the human agent the full conversation, the documents it consulted, and its own confidence, so the customer never has to repeat themselves. Sentiment signals push frustrated conversations to a human earlier, before the customer gives up.

Evaluation runs against a fixed set of real historical tickets with known correct answers, so accuracy is measured on the client's actual traffic rather than a generic benchmark. That test set is what makes it safe to change prompts, swap models, or expand the knowledge base later — regressions show up before customers see them.