AI cost management

Usage instrumented and spend controlled, treated like cloud cost rather than an afterthought

Model usage is a metered bill, and an agent that runs unattended can run up a metered bill. We instrument every call, set budgets and caps per workflow, choose the model by the task rather than by habit, and report the spend next to the work it produced, so the question "is this worth it" has a number behind it.

What we build

  • Per-workflow usage tracking: tokens, calls, cost, and what they produced
  • Budgets, alerts and hard caps so a runaway loop stops itself
  • Model routing by task: the cheap model where it is enough, the capable one where it is not
  • A monthly spend report against the time the automation saved

What you keep

  • The instrumentation, running in your own accounts with the providers
  • The budget and alert configuration, documented
  • A quarterly review of model choices as prices and capabilities change

Where it shows up

Trades and home services
A cap on the after-hours intake agent so a spam night cannot become a large bill
Professional services
Model spend attributed per client matter so it can be billed or absorbed knowingly
Churches and nonprofits
A fixed monthly ceiling with a report the treasurer can read

Guardrails

We treat an agent that can act as a production system, because it is one. Every agent we deploy has these four things.

  • Defined task boundaries

    An agent does the job it was built for and nothing adjacent. The boundary is written down before it runs.

  • An audit log

    Every action the agent took, with what it saw and what it decided, in a log you can read.

  • A human approval step

    Anything that touches money or customer records waits for a person to say yes.

  • A rollback path

    If it goes wrong, there is a known way back, tested before launch, not improvised after.