Agents Makers

Customer Support team

AI roles for Support teams.

Customer Support is the first team most operators automate, and the one where a poorly scoped AI deployment shows up in CSAT inside two weeks. The job is not to reply faster. It is to land the right answer with the right tone, escalate cleanly, and feed every interaction back into the knowledge base so the next ticket is one click shorter.

A hireable AI Support role is structured the same way you would brief a senior support agent: a clear scope of what it owns, a tested response taxonomy, an escalation policy that names a human owner, and a measurable target on first-response time, deflection, and CSAT. Anything looser than that is not a role, it is a chatbot.

The operating model in Support.

  • Owns a defined queue, not the whole inbox

    The role is scoped to a named queue (Tier 1 questions, billing exceptions, post-purchase, etc.). Tickets outside scope route to your team unchanged. This is what lets the role go live in weeks instead of months and what protects CSAT during ramp.

  • Reads from the same KB your team does

    The role pulls from your Help Center, internal SOPs, and ticket history. There is no separate AI knowledge base to maintain. When you update a help article, the role updates with it. When the role escalates, the human sees the same context.

  • Escalation policy is signed in scoping

    Every role ships with a policy that names what gets handled, what gets escalated, and who receives the escalation. The policy is reviewed weekly during the first 90 days and tuned on real volume, not assumptions.

  • Measured against your existing CSAT and FRT

    Baseline is captured from your help-desk before launch. The role is targeted against the same metrics your team already reports to leadership. If the role is hitting them inside 90 days, you read it directly off your existing dashboard.

How it rolls out

The playbook a real Operating Partner runs.

  1. Phase 1

    Scope the queue, capture the baseline

    Pick the queue with the highest volume of well-documented questions. Pull 60 days of ticket history. Cluster the top 20 intents. The role's first capability set is built around those clusters.

  2. Phase 2

    Author the response taxonomy + escalation policy

    For each intent, author the response template, the tone, and the escalation rules. This is the most time-intensive step and the one that decides whether deployment week is calm or chaotic.

  3. Phase 3

    Shadow mode for one week

    Role runs on live tickets but does not send. Your team reviews drafts and approves or rewrites. By end of week one, false-positive rate should be under 5%.

  4. Phase 4

    Live on the queue, weekly tuning

    Role goes live. Weekly ops review of escalations, false positives, and CSAT delta. Tune the response templates. By week 4, first-response time should be visibly down on the existing CSAT dashboard.

  5. Phase 5

    90-day KPI review, expand or hold

    Read the contracted KPI off your dashboard. If the target range is hit, expand to an adjacent queue or add a capability. If not, the operational guarantee covers continued tuning at zero additional retainer cost.

AI in customer support works when the role is scoped tight, briefed like a hire, and measured on the metrics your team already reports. Every role below ships with that operating model attached.

90-day operational guarantee. We agree on the outcome KPI before launch. If we haven't hit it by day 90, we keep working free until we do.

How it works →

Pick a role. Start deployment.

Every role in this view is hireable, governed, and anchored to the fully-loaded cost of the equivalent hire.