Artificial Intelligence & Machine Learning
Private AI infrastructure, pipelines and integrations: models hosted and maintained on GPUs you control, connected to your own data, and kept inside Australia.
Private AI, engineered like the rest of your infrastructure.
AI is now an infrastructure problem as much as a software one. Language models and machine learning models need GPU capacity, careful hosting, monitoring and patching, and a clear answer to where your data goes. NGN designs, builds and runs that infrastructure on-premises, in colocation or in the cloud, sized to the work you actually need it to do.
On the business side, we connect AI to the systems your people already use, such as Microsoft 365, your CRM, your helpdesk and your document stores, so assistants answer from your own data, show their sources and respect the permissions people already have. Routine workflows can be automated, with a person approving anything that changes data.
Security and sovereignty are designed in from the first conversation: models hosted in Australia, access control and audit logging on every request, guardrails against prompt injection and data leakage, and nothing sent to third-party model providers unless you decide it should be. That helps you meet your obligations under the Privacy Act, including the automated decision-making transparency requirement that commences on 10 December 2026.
Why private AI with NGN
- Infrastructure engineers first: GPUs, networking, storage and Kubernetes run by the same team
- Hosted in Australia by default, with your data never used to train third-party models
- Open-weight models you can inspect, version and keep, rather than a black-box dependency
- Retrieval that respects existing permissions, so an assistant only sees what its user can
- Guardrails, access control and audit logging built in from the start, not bolted on
- Honest sizing: we will tell you when a smaller model or a hosted service is the better fit
AI & machine learning capabilities
GPU Infrastructure & Capacity
GPU servers and clusters on-premises, in colocation or in the cloud, sized from your real workload: model size, concurrency and response-time targets. We plan the capacity, build the platform, and keep drivers, CUDA and firmware current.
Model Hosting & Inference
Open-weight language models and your own machine learning models served behind a private API, with batching, autoscaling, quotas and health checks. New model versions are tested against your prompts before they go live.
Vector Databases & Retrieval
Your documents indexed in a vector store such as PostgreSQL with pgvector, kept in step with their sources and filtered by the permissions people already have, so every answer is drawn from the right material.
MLOps & Pipelines
Data pipelines, training and fine-tuning, evaluation, a model registry and CI/CD for models. Every release is versioned and reproducible, and is promoted only when it beats the version it replaces.
Monitoring & Drift Detection
Latency, throughput, GPU health and cost on one dashboard, plus drift and quality monitoring for models in production, so a model that slowly gets worse is caught before your users notice.
AI Assistants & Integrations
Assistants and agents connected to Microsoft 365, your CRM, helpdesk and document stores, answering with sources and automating routine workflows, with a person approving anything that changes data.
Guardrails & AI Security
Input and output screening, prompt-injection and data-leakage defences, least-privilege access for agents, secrets kept out of prompts, and an audit log of every request and response.
Private & Sovereign AI
Models, embeddings and logs hosted in Australia on infrastructure you control, with no prompts or documents sent to third-party model providers unless you choose to. Designed to help you meet your Privacy Act obligations.
Technologies & Tools
The AI and machine learning stack we build, host and maintain.
NVIDIA, CUDA and other product names are trademarks of their respective owners.
Ready to put AI to work, privately?
Talk to our engineers about where AI would help, what it would take to host it securely in Australia, and whether a smaller first step makes more sense.
Can we use AI without our data leaving Australia?
Yes. Open-weight language models can run on infrastructure in Australian data centres, whether that is your own servers, a colocation facility or an Australian cloud region. The model, your indexed documents and the logs all stay inside that boundary, and no prompts or files are sent to an overseas model provider unless you decide they should be.
Will our data be used to train someone else's AI model?
Not when the model is hosted privately: prompts and documents never reach a third-party provider, so there is nothing for anyone else to train on. Where a commercial AI service is the better fit for a task, we use the business or enterprise terms and settings that exclude your data from training where the provider offers them, and keep sensitive data out of it.
Do we need our own GPUs to run AI?
Not necessarily. It depends on how many people will use it, how fast answers need to be, and how sensitive the data is. Smaller models run well on a single GPU, and many organisations start with rented GPU capacity in an Australian cloud region, then move to their own hardware once steady usage makes that cheaper. We size it from expected usage rather than guesswork.
What is retrieval-augmented generation (RAG)?
RAG gives a language model the most relevant passages from your own documents at the moment a question is asked, retrieved from a vector database, so the answer is grounded in your material and can cite its sources. Your documents stay in your own store, the model is not retrained on them, and retrieval can honour the permissions people already have.
How do you protect AI assistants against prompt injection and data leakage?
With layers, because no single control is enough. User input and retrieved content are treated as untrusted; a policy gateway screens prompts and responses; retrieval only returns documents the user may see; agents get least-privilege access, with a person approving actions that change data; secrets never go into prompts; and every request and response is logged. We test the result with adversarial prompts before launch.
How does a private AI deployment help with the Privacy Act?
The Australian Privacy Principles apply to personal information in an AI system as they do anywhere else. Hosting privately helps you meet them: you know where the data is stored, who accessed it and why, and you can correct or delete it. If you use automated decision-making that significantly affects individuals, the transparency obligation that commences on 10 December 2026 also applies, so we document how and where AI is used.
Which AI models can you host and maintain?
Open-weight language model families such as Llama, Mistral, Qwen and Gemma, embedding models for search and retrieval, and conventional machine learning models built with tools such as PyTorch, scikit-learn or XGBoost. We choose by task, hardware and licence terms, and test a new model version against your own prompts and data before it replaces the current one.
Talk to us about private AI.
Tell us what you would like AI to do and what data it would touch. We'll come back with a practical, secure way to get there.
Australia
International
Location
Sydney, NSW, Australia
Hours
Open 24 hours, 7 days
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