AI support center

Challenge

A support team grows with the number of clients and the number of channels: email, messengers, internal chats. Some requests go unanswered simply because nobody saw them. Context is lost when a conversation changes hands between shifts, and the quality of an answer depends on how well one agent remembers the rules. Managers hear about an unhappy client after that client has left.

Solution

The system reads incoming requests: it works out the topic and the sentiment, finds the relevant material in the company knowledge base and offers the agent a draft reply. The base itself is built and kept current from the company's own documents, Google Docs included, so an answer follows current policy rather than one person's memory.

A second layer watches what is happening: unanswered requests are escalated, difficult and critical ones are flagged, and dozens of working chats are condensed into a daily digest. The final call stays with a person — the system never sends a message on its own and does not stand in for the agent.

Outcome

Requests stop falling between channels, and replies are drafted from current policy instead of personal recall. Managers see missed, escalating and overdue conversations on the day they happen, and new hires reach the front line sooner: prompts and the knowledge base double as on-the-job training.