AI consulting that ends in something running.
Most AI consulting produces a strategy and a pilot nobody uses. We find where AI actually earns its keep in your business, build it into the real work, and train the people who have to run it after we leave. Auckland based, working across Australia and New Zealand.
What you get instead
The complaint about AI consulting is always the same: a lot of thinking, very little running. Our engagements are built to end on the other side of that line.
Consulting decides what to build. Enablement makes it stick.
Artificial intelligence enablement is the half of this work that is not software. Most organisations buy AI seats and never find out whether anybody uses them on real work. The licences renew, the usage stays flat, and the assumption is that the tools underdelivered.
Usually they were never adopted. Enablement is the discipline of closing that gap: teaching the people who do the work how to use AI on their actual tasks, writing the knowledge down where the organisation can reach it, and measuring adoption instead of assuming it. We run it alongside delivery, because separating the two is why so many AI programmes stall the month after the pilot ends.
How an engagement runs
Four stages, in order. The first is free, and the second is deliberately small.
We find where it pays
The free AI Opportunity Audit looks at the actual work, the tools you already pay for, and where hours or margin are leaking. It ends in a ranked list of what is worth building, including the things that are not.
We build the first one
One workflow, scoped small enough to ship in weeks. Built into the systems your team already uses, not beside them. You judge whether to continue on something that runs, rather than on a proposal.
We train the people who run it
The handover is the deliverable. The team that does the work learns to operate, adjust and extend what we built, and the reasoning behind it is written down rather than living in one person's head.
We measure whether it held
Adoption gets measured, not assumed. If a deployment is not being used three months later, that is a failure we own and fix, not a line item you discover at renewal.
The work, in practice
Every deployment below is published in full, with what was built and what it changed.
MacroActive
AI enablement across the business, deployed on their own stack.
The Ecommerce Accelerator
An agentic operating layer behind a multi-brand ecommerce operation.
Nature Baby
A company brain and a skill library the team runs without us.
Medical conversational assistant
A clinical-grade assistant built under real privacy constraints.
Frequently asked questions
What does an AI consultant actually do?
A useful one finds where AI earns money or hours in your business, builds it into the real work, and trains the people who do that work to run it without them. The unhelpful version audits your organisation, writes a strategy, and leaves before anything is deployed. We do the first kind: every engagement ends with something running in production and a team that can operate it.
How much does AI consulting cost in Australia and New Zealand?
It depends on scope, but the honest ranges are: a discovery engagement to work out what is worth building, a fixed-scope deployment for a single workflow, and an ongoing enablement retainer once several are live. We publish a free AI Opportunity Audit first precisely so nobody buys a scope before knowing what the return looks like. If the numbers do not work, we say so.
What is artificial intelligence enablement?
Enablement is the half of AI that is not software. It is making sure the licences you already pay for are actually used, that the people who do the work know how to use them on their real tasks, and that the knowledge lives in the business rather than in one enthusiast's head. Most organisations buy AI seats and never measure whether anyone touches them. Enablement is the discipline of closing that gap.
What is the difference between AI consulting and AI enablement?
Consulting decides what to build. Enablement makes sure it gets used after it is built. Doing only the first produces a strategy nobody follows; doing only the second optimises tools nobody chose deliberately. We run them together because separating them is why so many AI programmes stall after the pilot.
How long before we see something working?
The first deployment should be live in weeks, not quarters. We deliberately scope the first piece of work small enough to ship and visible enough to judge, so you can decide whether to continue on evidence rather than on a proposal. A programme that needs six months before anything runs is a programme that cannot be corrected.
How is this different from a big-four AI strategy engagement?
Scale and accountability. A large firm can field a bigger team and will produce a more thorough document. We build the thing, hand over the working system, and are still there when it breaks. If what you need is a board-ready strategy deck, a large consultancy is a reasonable buy. If you need AI running in the business by the end of the quarter, that is our work.
Do we need our own data infrastructure before we start?
No, and waiting for a data programme to finish is one of the most common reasons AI never ships. Most valuable early work runs on the documents, tools and systems you already have. Where sensitive data or residency rules make the cloud the wrong answer, we deploy private models on infrastructure you own instead.
Can you work with our existing Microsoft or Google stack?
Yes. Most of our deployments sit on top of what a business already pays for: Microsoft 365 and Copilot, Google Workspace and Gemini, Claude, ChatGPT. Buying a new platform is usually the wrong first move. The cheaper and faster path is getting real work out of the licences already on the invoice.
Do you work with Australian businesses from New Zealand?
Yes. We are Auckland based and work across Australia and New Zealand, on site where the work needs it and remotely where it does not. Several of our published deployments are with Australian businesses. Australian privacy obligations and data residency requirements are part of how we scope, not an afterthought.
What happens if AI is not the right answer?
We tell you. Some processes are better fixed with a rule, a form or a conversation than with a model, and recommending AI for those is how consultancies lose clients in year two. The audit is designed to find the cases where the answer is no as reliably as the cases where it is yes.
Find out what is actually worth building
The AI Opportunity Audit is free, takes a short conversation, and ends in a ranked list of where AI earns its keep in your business. Including, where it applies, the answer that it does not.