Customer Support
AI use cases for Customer Support
Answer faster and learn from every ticket — without losing the human in the loop.
3 use cases
These are the customer-support workflows where AI earns its keep — answering faster and learning from every ticket. That includes drafting and triaging replies from your knowledge base, building an internal support knowledge agent for your reps, and analyzing ticket trends to suggest new macros. Each use case is scored by ease, impact, and risk, with a written guide for doing it using Codex, Claude Code, or Claude Cowork — and an agent reviewing anything that reaches a customer.
Build an internal support knowledge agent
An agent that answers your support team's questions from your own help docs, macros, and past tickets, with a citation on every answer so reps can verify before they reply. It speeds up handle time and onboarding without putting an unverified bot in front of customers.
Draft and triage support replies from your knowledge base
An agent reads each incoming ticket, classifies and routes it, then drafts a reply grounded in your own help docs with citations — leaving your team to review, edit, and send. You own the whole pipeline and can fix or extend it without us.
Ticket-trend analysis and macro suggestions
Turn your raw ticket exports into a weekly read of what customers are actually contacting you about, then use those clusters to spot missing or stale macros. The output is a report and a script your support team owns and runs itself.
AI for customer support — common questions
What support work can AI help with?
Drafting and triaging replies from your help docs, giving reps an internal knowledge agent that answers from your own content, and mining ticket trends to suggest macros. Every answer is grounded in your documentation, not the model's general training.
Is it safe to let AI answer customers directly?
Keep a human on the send button. These guides draft and triage; a person approves anything customer-facing. The real cap on quality is your help docs, not the tool — most teams find that out fast, which is a good thing.
Which support use case should we start with?
Ticket-trend analysis and an internal knowledge agent are lower-risk because they are internal. Customer-facing reply drafting is worth doing too, but start it with a human reviewing every send before you widen it.
Want help shipping one of these?
We'll build it with your team, on your real work, and leave you owning it.