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Agent Deployment

Get the work off your team's table.

Most AI programmes make people faster at work they still have to do. We find the processes you run thousands of times a year, build agents that own them end to end, and run them in production.

Agent fleet

Live

Invoice intake

Finance

Running

Order confirmations

Supply chain

Running

Stock reconciliation

Operations

Awaiting approval

Vendor follow-ups

Procurement

Running
The gap

Adoption is not delegation

Only 23% of companies are scaling an agentic system in even one business function, and rarely past a tenth of the work inside it. Using AI and handing work to AI are different problems, and the second one is an engineering problem.

It never decides alone above your threshold

A refund over the limit, an account with a risk flag, a request too ambiguous to act on. The model proposes and policy decides, so anything costly waits for a person. Suspended runs keep their state, and an approval hours later resumes exactly where the run stopped.

A trace that answers why did it do that?

Real processes branch. Two lookups run in parallel, a policy check joins them, and a value test decides which way the run goes. Every fork, every retry and every decision is recorded, so when something expensive happens the answer is on screen rather than in someone's memory.

How we work

Four steps, and the last one is the long one

The first three get an agent working. The fourth makes it survivable in production, and it is where most of the engineering goes.

  • 01 · Find

    Locate the work worth taking

    Volume, cost per run, cycle time, error rate. If it cannot be measured today, we cannot prove we improved it tomorrow.

  • 02 · Build

    Constrain what the model can do

    Typed tools, validated arguments, output shaped into structures your systems accept. The agent proposes; the schema decides.

  • 03 · Deploy

    Connect it to real systems

    Scoped permissions and only the data the work needs. An agent earns production access the way a person does, with an audit trail behind every action.

  • 04 · Operate

    Make it trustworthy under failure

    Idempotent retries so a crash cannot double-apply an action, and golden-set replays that catch quality drifting before your customers do.

FAQ

Working with us

What work would your team happily never do again?

Bring us the process. We will tell you honestly whether an agent should own it.