Chat interfaceSign-in through single sign-on and MFAManaged
Model filesChecksum verified against sourceManaged
Prompt logsRetention period to approveDecision needed
Document indexFolder permissions carried into searchManaged
Illustration · not live data
01Who it's for
Is this service right for you?
It suits organisations that can't send client or sensitive information to public AI services, or must keep data in Australia, but still want AI over their own documents.
Built for
Legal, health, accounting and finance practices
Government, council and education teams
Businesses with 5 or more staff that want AI over internal documents without a per-user cloud subscription
Organisations whose own security policy restricts where data may be processed
You probably need it if…
Client confidentiality or professional rules stop you using public AI for client work
Government, health or education requirements say data must stay in Australia
Customer contracts restrict where their data may be processed
You want staff to search internal documents with AI without sending them to a third party
02What's included
What you get
Design & deploy
Use-case and sizing workshop
We agree what the AI needs to do, how many people will use it and which documents it should draw on. That sets the model size, the hardware and whether on-premises or private cloud suits you.
Model selection and deployment
We choose open-weight models suited to your task and licence needs, then deploy them on your hardware or in an Australian private cloud, with a simple chat interface and an internal application programming interface (API) for other tools.
Private RAG over your documents
Retrieval-augmented generation (RAG) lets staff ask questions and get answers drawn from your policies, contracts, manuals and knowledge base, with links to the source documents. Retrieval is designed to respect who is allowed to see each document.
Harden & control
Hardening and network isolation
The model host is isolated on your network, outbound internet access is blocked or tightly limited, and the interface sits behind single sign-on and multi-factor authentication (MFA). We disable telemetry and remove services the deployment doesn't need.
Logging and access control
Prompts, responses and document retrievals are logged to your monitoring, with retention you choose. Access is granted by role, so an AI assistant for HR can't answer questions from finance documents.
Hardware
Hardware supply and configuration
If you need a graphics processing unit (GPU) workstation or server, we source it, configure it securely and install it through our IT hardware service, so one team is responsible from the box to the working assistant.
Secure AI
Keeping your document index as private as the documents
Private search builds an index from your files, and that index can reveal what the files say. We design search to respect your existing folder permissions, so answers are drawn from documents staff are already allowed to open.
03In detail
Local AI & private LLMs, in depth
Why does private RAG need its own access controls?
Retrieval-augmented generation (RAG) splits your documents into passages and indexes them, so the model can answer from your own content. For example, an HR assistant could answer “How much parental leave do we offer?” from your current policy, with a link to the clause, while declining to use documents the person asking isn’t allowed to open.
That raises the main security question for any RAG system: who can see what. A RAG system that ignores document permissions can hand a junior staff member a summary of a board paper. The OWASP Top 10 for LLM Applications lists vector and embedding weaknesses as a distinct risk for exactly this reason, so we build retrieval to respect the same access rules as the original documents.
How do we check the model itself is safe to run?
Open-weight models are downloaded files, so they carry supply chain risk (LLM03 in the OWASP Top 10 for LLM Applications). We take model files only from their official source, check them against published checksums and use safe formats such as safetensors. The rest of the hardening treats the model host like any other sensitive server.
The framework mapping below shows how these controls line up with ISO/IEC 27001 and ISO/IEC 42001, which matters if you report to a board or a government buyer. Our AI security and governance service explains those standards and can add the policy and risk register around the deployment.
Should you buy hardware or use private cloud?
If buying hardware suits you, our IT hardware procurement service sources the workstation or server, configures it securely before delivery and installs it. If you would rather not buy hardware, a dedicated environment in an Australian private cloud is the alternative. We recommend the provider and region after discussing your needs.
If you are still deciding whether local AI is the right path at all, our AI consulting service compares it with cloud options for your use case.
04The process
How it works
1Step 01
Scope and size
We agree use cases, users and documents, then recommend a model size, hardware and hosting option with a written quote.
2Step 02
Pilot
We deploy a pilot with a sample of your documents so a small group can test answer quality on real questions.
3Step 03
Harden and test
We lock down the host, connect logging and test for prompt injection and access-control bypass before wider use.
4Step 04
Roll out
The assistant goes live for the agreed users, with a short usage policy and training.
5Step 05
Maintain
If you choose ongoing support, we patch the host and software, review logs and update models and document indexes as your needs change.
05Frameworks
How this maps to the frameworks
Useful for your auditor, board or procurement team. The mapping shows where this service supports each framework. It doesn't replace a formal assessment.
Local AI & Private LLMs: framework mapping
Control
ASD Essentials
ASD maturity model (current)
ISO/IEC 27001
NIST CSF
ISO/IEC 42001
Network isolation of the model host
Boundary services
–Not applicable
A.8.20, A.8.22
Protect (PR.IR-01)
–Not applicable
Access control for AI and document retrieval
Identity services
Multi-factor authentication
A.5.15, A.8.3, A.8.5
Protect (PR.AA-05)
–Not applicable
Model host and software patching
–Not applicable
Patch applicationsPatch operating systems
A.8.8
Protect (PR.PS-02)
–Not applicable
Prompt and retrieval logging
Visibility and response
–Not applicable
A.8.15
Detect (DE.CM-09)
A.6.2.8
Model provenance and data sources
–Not applicable
–Not applicable
A.5.21
Govern (GV.SC-07)
A.7.5
06Pricing
Pricing and how to start
Pricing
Scoped after a strategy call
What changes the price
Price depends on the number of users and assistants, the volume and type of documents, the model size, whether hardware is bought or private cloud capacity is rented, and whether you want us to maintain the deployment afterwards. Hardware is quoted separately.
Use-case and sizing workshop
Deployment of an open-weight model with a chat interface
A private LLM is a large language model that runs on infrastructure you control, such as your own server or a dedicated private cloud environment, instead of a public AI service. Prompts, documents and answers stay within your environment. Private LLMs usually use open-weight models, whose model files are published for organisations to download and run themselves under the model's licence.
Is a local AI model as capable as ChatGPT or Copilot?
Usually not for every task, but often good enough for the tasks that matter to you. The largest public models remain more capable for broad, open-ended work. For focused jobs, such as answering questions from your own documents, summarising or drafting, a well-chosen open-weight model can do the job. The pilot tests this on your real questions before you commit.
What is private RAG?
Private RAG, or retrieval-augmented generation, connects a language model to your own documents. When a staff member asks a question, the system finds the most relevant passages, gives them to the model and returns an answer with links to the sources. Everything runs on your infrastructure, and retrieval is designed to follow document permissions, so people get answers only from documents they are allowed to see.
Does our data stay in Australia with local AI?
Yes, when the model runs on your own hardware in Australia or in a private cloud environment hosted in Australia. We design the deployment so prompts, documents and logs are stored and processed only there, and we block outbound connections the system doesn't need. You can verify this, because the infrastructure is yours or dedicated to you.
What hardware do we need to run local AI?
It depends on the model size and the number of people using it at once. Small pilots can run on a single workstation with a capable graphics processing unit (GPU). Larger models and more users need a dedicated server with more GPU memory. We size the hardware after the use-case workshop, then source and configure it for you.
How do you secure a local AI deployment?
We treat the model host like any other sensitive server: network isolation with restricted outbound access, single sign-on with MFA, access by role, logging, patching and verified model files. Before staff use it, we try to break it, including attempts to get around document permissions.
