AI for customer support
Triage, retrieval, and draft-and-review. Your support team gets faster and more confident — your customers keep talking to humans.
客户支持团队交给我们的三类工作
Ticket triage
Route, tag, prioritize. Catch the urgent stuff.
Internal retrieval
Agents query the kb in plain English, with cited sources.
Draft-and-review reply
AI drafts in your voice. Agent edits. Sends.
我们为客户支持团队搭建的内容
Ticket triage
Route, tag, prioritize. Catch urgent stuff that buried itself in the queue.
Internal retrieval
Agents ask plain-English questions of your knowledge base, runbooks, and product docs. Answers cite their source.
Draft-and-review reply assistant
AI drafts a response in your voice. Agent reviews, edits, sends. Quality up, time down.
Macro / template generation
Surfaces repeated issues. Suggests new templates. Pruning the macros library that's grown 200+ entries.
常见问题解答
We tried a customer-facing chatbot once and it went badly — how is this different?
We don't build customer-facing chatbots by default. the work points inward: triage, retrieval, draft-and-review. agents stay on the phone or in the ticket; the ai sits behind them. customers keep talking to humans.
How is this different from zendesk ai, intercom fin, or the gorgias suggestions we already pay for?
Those tools work inside their own walls — your zendesk macros, intercom articles, gorgias replies. we build retrieval across the rest: 4 years of historical tickets, the product docs, the shipping policy pdfs, the engineering runbooks.
How do you handle the agents who think this is the first step to replacing them?
We say so on day one. the framing is 'helps agents, not replaces them' — and we measure it. the kpis we move are first-response time and resolution quality on complex tickets, not headcount. training sessions with the support team are part of the build, not an afterthought.
