What to fix first
A conversation of up to two hours and a written plan: what is costing you, what to change, and the order to change it. The plan always includes the things you can do yourself, at no cost, without us. NZ$1,000 + GST.
Service
Put AI to work. Trust the output.
We take the routing, chasing and re-keying off your team, using the software you already run, and make sure you can show how the work was done.
Your best people are doing work that does not need them
A form that needs three approvals and sits in an inbox for four days. The same job details typed into a quote, a job sheet and an invoice. Documents that have to be opened, read and filed before anything can start.
None of it needs their judgement. All of it needs their attention, and attention is the thing you have least of.
What we fix
Documents that have to be read before anything can happen. An AI agent reads the incoming email, quote, invoice or report, pulls out what matters and sends it where it needs to go. Your team checks the result in seconds instead of doing the reading.
Work that stalls between people. Approvals route themselves and get recorded. Registers update as a by-product of the job. Nothing sits waiting for someone to remember to check an inbox.
Whole processes, not just single steps. An agent can carry a job from the first email through to the final record, working each step in turn and stopping for a person where the decision actually matters. You approve, correct or override, and it carries on. The process runs without anyone driving it, and you still own every call worth owning.
AI tools you already pay for and barely use. Most businesses have ChatGPT or Claude returning a fraction of what they could. That is usually the cheapest thing to fix, and finding out costs you nothing.
We build inside the Microsoft 365 you already own wherever we can, so most of this needs no new licence and no procurement.
A faster bad process is still a bad process
Automating something you have not examined locks in every problem it already had. The approval that went to the wrong person now goes there automatically, and it is harder to change than it was before.
We look at the process first. Sometimes the answer is not automation at all and a step just gets deleted. That is a cheaper result than building something clever that should not exist.
Sooner or later, someone asks how the work was done
A client, an insurer or a regulator, and always after the fact rather than during it. We make sure you can answer, using the risk process you already run rather than a separate regime nobody maintains.
That is AI governance. It comes as part of a build, and it can also be done on its own.
What you end up with
- Capacity returned to the people who were doing the chasing
- Documents read and acted on without a person opening each one
- Approvals that route themselves and get recorded without anyone following up
- Information entered once instead of retyped into three systems
Where to start
Common questions
What is an AI agent?
Software that works through a task the way a person would, rather than following a fixed set of rules. It reads what it is given, works out what to do next and does it. Where it is unsure, or where you have said a person must decide, it stops and asks. That is what lets agentic AI handle a messy inbox or an unstructured document, which a rule-based workflow cannot.
Does this replace people?
It takes tasks, not jobs. What we put on an agent is the routing, reading, chasing and re-keying that already frustrates the people doing it, and every agent we deploy has a person checking or approving where the decision matters. What you do with the time it gives back is your decision, not something the technology makes for you.
Do we need to buy new software?
Usually not. If you have Microsoft 365 you already have Power Automate and SharePoint, which covers most of what a mid-sized business needs. Where you already run Nintex or Flowingly, we build the workflow automation there instead.
How do we know we can trust the output?
We only put AI on work where a person can check the result quickly, and we design the process so that check actually happens. That is a limit on what we deploy, not a promise we make afterwards.
Do you build the AI itself?
We work out what is worth automating, design the process and put the agents into it. Deep technical build goes to named specialist partners, and we say so rather than stretch a claim across it.