Most AI tools quietly ask you to hand over your data. We build the opposite: AI agents and automations that run in controlled, client-owned environments — where your business data is never fed into public models and never used for training.
Kanaky Tech is a Pacific AI automation and agent development studio that builds private AI systems — AI agents and automations that run in controlled, client-owned environments where business data is never fed into public or consumer models or used for training. These systems are designed for organisations with confidentiality, compliance or digital-sovereignty requirements: institutions, public-sector bodies, agencies handling client data, and regulated SMEs. Engagements run private-by-default and under NDA, so the AI you deploy is AI you actually own.
When a staff member pastes a contract, a patient record, a client brief or a financial model into a consumer chatbot, that data leaves your control. You rarely know how long it is retained, whether it trains the next version of the model, or who inside the provider can review it. For most businesses that is an inconvenience. For institutions, regulated firms and agencies bound by confidentiality, it is a breach waiting to happen.
The risk is not theoretical. The most common AI data-leakage incident is not a hacker — it is an employee being helpful with the nearest tool. The fix is not banning AI; it is giving people AI they are allowed to use.
A private AI system answers all three at once. Instead of choosing between productivity and protection, you get an environment where AI is genuinely useful and the data never goes where it shouldn't. If you are weighing where AI fits in your operations first, start with an AI opportunity audit — then we build the private layer around the use cases that survive scrutiny.
Your data is never used to train public or consumer models. We use provider configurations and architectures that switch training off — your inputs stay inputs.
The system runs inside boundaries you define — dedicated deployments, private cloud or self-hosted — not a shared consumer app where data mingles with everyone else's.
The configuration, the prompts, the agents and the outputs belong to you. No lock-in to a black box you can't inspect, export or move.
Engagements run private-by-default and under NDA. We treat your data, your processes and the existence of the project as confidential from the first conversation.
Where the use case demands it, we deploy local or self-hosted models on infrastructure you control, keeping sensitive workloads off external providers entirely.
Access, actions and outputs can be logged and reviewed. You can show a regulator, a client or your own board exactly what the system did and with what data.
Private AI isn't a luxury upgrade — for some organisations it's the only legal way to use AI at all. The common thread is data that carries an obligation.
| Who | The data problem | What private AI changes |
|---|---|---|
| Institutions & public sector | Citizen, administrative and policy data that can't leave controlled boundaries. | AI agents and automations that stay inside sovereign or self-hosted infrastructure. |
| Agencies handling client data | Confidential client briefs, assets and strategy bound by NDA and client contracts. | A controlled environment per engagement — no client data in consumer tools. |
| Regulated SMEs | Legal, accounting, health and finance records governed by sector rules. | No-training, no-retention configurations with auditable access and outputs. |
| Confidentiality-first businesses | IP, R&D, M&A or commercially sensitive work that can't be exposed. | Private-by-default systems, NDA from day one, full ownership of the build. |
Many of these organisations already want the productivity of business process automation and the leverage of custom AI agents — they just can't accept the data trade-off public tools demand. Private AI removes that trade-off.
Kanaky Tech operates from Nouméa and Auckland, serving the wider Pacific — a region where who controls data and infrastructure is a live, practical question, not a slide in a deck. That context shapes how we build private AI, and it's grounded in real, live systems:
To be honest about scope: our public Pacific work centres on open-data infrastructure and local AI deployment, while private engagements for businesses and institutions run under NDA and aren't named. The same principle runs through both — data and control stay where they belong. You can see the broader picture in our case studies.
We map where AI would help, classify your data by sensitivity, and surface the confidentiality and compliance constraints that shape the build.
We choose the architecture — local, private cloud or hybrid — so sensitive data stays controlled while non-sensitive tasks stay efficient. No-training, no-retention by design.
We build the agents and automations inside that environment, with access logging and outputs you own outright — ready to show a board, a client or a regulator.
We hand it over, train your people on the safe path, and embed the system into daily work so staff stop reaching for the leaky consumer tool.
It's the same process we run across every engagement — starting from an AI opportunity audit and ending with something your team actually uses. The private-AI difference is that confidentiality is a design input from step one, not a patch at the end.
A private AI system is an AI agent or automation that runs in a controlled, client-owned environment instead of a public consumer tool. Your business data stays inside boundaries you define, is never fed into public or consumer models, and is never used to train anyone's AI. You own the outputs and the configuration. It is the difference between renting AI on someone else's terms and operating AI you actually control.
No — never. In a Kanaky Tech private AI system, your business data is never used to train public or consumer AI models. We work with controlled environments and provider configurations that disable training on your data, and engagements are available under NDA by default. Your data is an input to the work, not a training set for someone else's model.
Yes. We design controlled environments where sensitive data does not leave boundaries you control. Depending on the use case that can mean local or self-hosted models, private and dedicated provider deployments with no-training and no-retention settings, or a hybrid where only non-sensitive context ever reaches an external model. We choose the architecture to fit your confidentiality and compliance needs, not the other way around.
Organisations that handle data they cannot paste into a public chatbot: institutions and public-sector bodies, agencies handling client data under confidentiality, and regulated SMEs in fields like legal, accounting, health or finance. If your data carries confidentiality, compliance or digital-sovereignty obligations, you need AI that runs privately.
Yes — as options, scoped honestly to your situation. We can deploy local or self-hosted models on infrastructure you control, configure dedicated private cloud environments, or keep sensitive workloads on local and sovereign infrastructure while only non-sensitive tasks touch external providers. We will tell you plainly which option fits your data, your budget and your compliance requirements rather than overselling a single model.
Using a public chatbot at work means staff paste business and client data into a consumer tool you do not control, with no guarantee about retention, training or who can see it. A private AI system inverts that: the environment is controlled and client-owned, data never enters consumer models or training sets, access and outputs are auditable, and the whole thing is built around your specific processes instead of a generic chat window.
If your data can't go to a public model, let's build the private system that lets you use AI anyway — controlled, auditable, and yours. Start with a free audit.