Questions,
answered.

Six to eight weeks from scoping call to a deployed system handling real work. Week 1 is process mapping, where we sit with the people doing the work and watch what actually happens rather than what the process document says. By the end of Week 2 you have a working automation running in production, usually the highest-volume, lowest-risk workflow. Weeks 3 to 6 cover the rest, plus error handling, retries and alerting.

By Friday of week 2 you get a deployed automation doing real work, not a demo on a slide. It will be narrow in scope and rough at the edges, but it runs, it handles your actual data, and you can watch it work. If we cannot show you that by week 2, something is wrong with the scope and we would rather find out then than in month three.

For a single well-defined workflow, sometimes. A focused integration between two systems can ship in two to three weeks. What we will not do is compress the process mapping phase, because that is where the expensive mistakes get avoided. Skipping it to save a week usually costs a month.

It depends on scope, and we quote after a discovery call rather than guessing on a pricing page. What we commit to is fixed scope in AUD, quoted before we build. No hourly billing that quietly expands, no change requests discovered after the invoice. If the scope genuinely changes, we requote before doing the work, not after.

Count the hours. If a person spends ten hours a week moving data between systems, that is roughly five hundred hours a year of work that produces no value. During the free audit we map that honestly, including the cases where the answer is that automating it is not worth the cost. We would rather tell you that on the first call than three months in.

Only if you want one. You own the code, the infrastructure and the documentation from day one. If you never speak to us again, the system keeps running and your team can extend it. Some clients want ongoing support and we offer it, but we do not build systems that only we can maintain.

Three things. Agents act through a defined set of tools rather than having free rein, so the range of possible actions is bounded. High-consequence actions, anything that moves money or contacts a customer, require human approval. And every decision is logged with the reasoning that produced it, so when something goes wrong you can see exactly why rather than guessing.

Often, no. If a workflow is fully deterministic, an AI agent is the wrong tool: slower, more expensive and less predictable than a webhook. A large part of our job is talking clients out of using AI where a plain integration would do the same thing more reliably for a tenth of the cost. We reach for AI when the input is genuinely unstructured or the judgment is genuinely ambiguous, and not before.

n8n for most production workflow automation, because it is self-hostable, version-controllable and does not price by task volume. OpenAI and Claude for language work, LangGraph for multi-step agents. Xero and MYOB APIs for finance automation. Make.com and Zapier where they are genuinely the right answer, and we will tell you when they are rather than selling you something bigger.

Thirty minutes. We map how your operation actually runs, name the two or three highest-leverage automations, and tell you roughly what each would cost in AUD. No deck, no pitch, no follow-up sequence. If we think you should not automate anything yet, we will say that too.

Our engineering team overlaps with Australian business hours for four to six hours every day. Standups, demos and calls happen in your morning. Everything else is async, with a written update at the end of every working day, so you never have to wonder where things are.

We build to the Australian Privacy Act 1988 (Cth) and the Australian Privacy Principles. Data can be hosted in AWS ap-southeast-2 in Sydney on request, so it never leaves the country. High-risk fields are excluded or masked by default rather than on request, and you own the infrastructure the data sits on.