You're paying for AI. Your team is barely using it.
Your team already has Claude, ChatGPT, Copilot and Gemini. We train them to actually drive these tools and shape them around how your team really works, so the subscriptions you pay for start earning their keep.
We specialise in maximising usage of these tools
The licences are bought. The usage is not.
Most businesses are already paying for AI. The licences are bought, the usage is not there. People dip into ChatGPT for the odd email, never touch Copilot inside the tools they live in, and have no idea what Claude can really do.
We close that gap. We get your team driving these tools on their real work, build the prompts and workflows that make good output repeatable, and track the hours it gives back. The point is not a course. The point is a team that quietly gets more done.
We have found that organisations are only getting nine percent out of the AI subscriptions they are already paying for.
James OldhamCEO, Sentry AIWhat we actually build
We do not just prompt agents. We build the harness around the agents your team already use, so we can govern who sees what, ground them in your real data and workflows and keep them working when the models change.
That means structuring the skill and context files that turn a general assistant into one that knows your business, over the AI organisation you already pay for. Claude, ChatGPT, Copilot or Gemini, whichever you already run, and we will tell you which licence is not worth renewing.
Skill files
A folder, not a saved prompt. The job, how your business does it, and the templates and rules it reads from.
Context files
What the model cannot know: your products, your pricing logic, your terminology, your tone. Every skill reads from them.
Role design
Which skills and context each person gets, so everyone opens the tool already set up for their job.
Role-based access control
What the assistant can reach, not just what it can do. Permissions per person, so finance data stays with finance.
The harness
All of it wired over your own Claude or Microsoft organisation. Models change; the harness keeps working.
A harness, as it is actually structured
One folder per job, each carrying its own instructions and the material it reads from.
Teams who were not going to use it
An organic baby brand had Claude, and no real way to use it.
A merchandising process ate a full day every week and their core data was locked in a legacy system. We built a Claude coworker layer over the SharePoint they already ran, with role-based access. More than ten people across merch, procurement and operations now query live data themselves, in plain English.
Full day
Process, before
~1 hr
Process, after
10+
Staff querying data
Frontier AI in front of clinical staff, without the data leaving the country.
A medical network of 15 practices rolled out Claude Desktop across its admin team, clinical staff, leadership and board. We built the harness over their own Claude organisation and routed inference through a custom LLM gateway to AWS Bedrock, so patient data never left the region.
4 groups
Admin to board
15
Practices in network
In region
Data residency
Questions we get asked
Our team is not technical. Is that a blocker?
No, that is the starting point. Every rollout we run assumes no internal AI capability and non-technical staff. The work is building the harness so people do not need technical skill to get value: they open the tool and their role is already set up for the job they do.
How is this different from an AI training course?
A course teaches people to prompt and leaves. We build the skills, context files and role design over your own organisation, so the value is in the system rather than in what somebody remembered from a session. Training alone does not survive contact with a busy week.
We tried ChatGPT and it never stuck. Why would this be different?
Because nothing was built. Without context files the model does not know your business, and without role design everyone starts from a blank box and gets a different answer. Adoption fails for structural reasons, not because people lacked enthusiasm.
Which AI tools do you work across?
Claude, ChatGPT, Microsoft Copilot and Google Gemini. We work with whatever you already pay for rather than moving you onto something new, and we will tell you when a licence you are holding is not worth renewing.
How long before we see hours back?
The harness goes in within the first couple of weeks, and that is usually where the first hours appear. The larger gains come from the return visit, once we can see what people actually adopted and build around it.
How much of our team's time does it cost?
Short sessions per team rather than days out of the business. We are watching the work, not running a classroom, so most of the effort sits with us.
Can we control what each person's assistant can reach?
Yes, through role-based access control. Permissions are set per person on top of your existing directory, so the assistant only reaches what that role is allowed to see. It is the difference between handing a team a powerful tool and handing them the whole filing cabinet.
Does our data stay private?
It can stay fully in region. For a medical client we routed inference through a custom LLM gateway to AWS Bedrock, so patient data never left their boundary. Where sovereignty is the deciding factor we build the infrastructure to match.
Do you work with businesses outside Auckland?
Yes. We are Auckland based and run rollouts across New Zealand and Australia, on-site or remotely.
Find your fastest win with AI
The free AI Opportunity Audit shows you exactly where the AI you already pay for will pay off first, in about two minutes.