Use case

AI for customer support

Triage, retrieval, and draft-and-review. Your support team gets faster and more confident — your customers keep talking to humans.

What teams hire us for

Three jobs Support teams hand to us

  • 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.

What we build

Things we build for Support teams

  • 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.

Time lost, made visible

Start with the hours your Support team loses

We find where the team loses time, show what it costs, and build the fix. Three services take you from finding the jobs to your team using the result.

  • AI consulting

    Find where AI pays off: the jobs ranked, what each costs today, and what to do first.

    Learn more
  • AI implementation

    Put it to work and keep it running: AI does the volume, a person checks the result.

    Learn more
  • AI training

    Make the team use it: role-specific training tied to the workflows we build.

    Learn more
Questions

Common questions

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.

Tell us which job takes the most time.

30 minutes. No preparation needed. We will tell you honestly whether AI can help, and where to start.