Service · AI Agents & Automation

The work that repeats should not need a person

Intake, triage, quoting, chasing, and reporting follow rules your team already knows. We put those rules into agents that work the queue, and keep a person in the loop wherever money or reputation is at stake.

  • Every agent has a written escalation rule to a named human
  • Actions are logged, so you can audit what the system decided
  • We start with one queue, measured, before adding a second
  • Runs on your accounts, with your data staying in your systems

What you get

Deliverables, not slideware

  • 01

    Queue selection and measurement

    We pick the queue with the most repeat volume, count the current handling time, and set the number we are trying to move.

  • 02

    Agent with guardrails

    Defined scope, refusal rules, escalation path, and a log of every action so nothing happens off the record.

  • 03

    Human handoff surface

    When the agent escalates, the person receives the case with context attached instead of a raw thread to read.

  • 04

    Ongoing evaluation

    A review set of real cases run against every change, so improvements are proven before they reach customers.

agent-scope.md
  • 01In scope: order status, delivery windows, invoice copies
  • 02Out of scope: refunds, contract terms, anything priced
  • 03Escalates to: named human, with full thread attached
  • 04Logs: every question, answer, and refusal, timestamped
  • 05Review cadence: 25 real cases, weekly
Sample of the deliverable. Every agent ships with a scope document like this one.

How it runs

What actually happens, step by step

No discovery theatre. Each step ends with something you can read or use.

  1. 01

    Pick the queue

    One queue with real repeat volume, counted before anything is built.

  2. 02

    Write the rules

    Scope, refusals, and escalation path agreed in plain language with the people who do the work.

  3. 03

    Ship with guardrails

    The agent goes live on a narrow scope, logging every action from day one.

  4. 04

    Measure and widen

    Real cases reviewed weekly. Scope only grows once the numbers hold.

Tangible

What lands in your hands

Named objects with a format and a week attached, so the handover is easy to picture.

  • Working automation

    One process running end to end, on your data, with a human approval step.

    Deployed servicePhase one
  • Operating runbook

    What to do when it fails, who to call, how to roll back.

    PDF · 8 pagesFinal week
  • Evidence dashboard

    The numbers that prove it worked, refreshed daily.

    Live URLWeek 5
  • Source repository

    Yours from day one, including the prompts and the evaluation set.

    Git repo + READMEWeek 1
Sample

Operating Runbook

Failure modes, rollback steps, and who owns each alert.

8 pp
Download the sample (PDF)

This is the real template, with client figures replaced by representative ones. Read it before you talk to us — if the format is not useful to you, the engagement will not be either.

Before and after

What the change looks like in the week

Red is the cost you carry today. Green is what the system gives back.

Today

  • The same twenty questions answered by hand every day
  • Follow-up depends on someone remembering to follow up
  • Quotes stall in an inbox over a weekend
  • Nobody can say how many requests were handled last month

After

  • Repeat questions answered immediately, with a record
  • Follow-up runs on schedule whether or not anyone remembers
  • Quotes leave the same day, escalated only when unusual
  • Volume, response time, and escalation rate on one screen

Objections

Answered before you ask

What if the agent gets something wrong?

It escalates instead of guessing on anything outside its defined scope, and every action is logged. We tune the scope against real cases before it touches customers.

Will customers know they are talking to a system?

Yes. We do not build agents that pretend to be a named employee. Being clear about it costs nothing and prevents the complaint that matters.

Do we need to replace our current tools?

Usually not. Agents work on top of the systems you already pay for. Replacement is a separate decision with its own arithmetic.

One next step, and it is a paid one on purpose

The AI Opportunity Diagnostic is a fixed-scope engagement. You leave with a ranked plan you can act on with us or without us.

Still sizing the problem? Write to us in your own words first. A person reads it.