PRIVATE AI FOR
SENSITIVE
ORGANISATIONS
AIRGAP LLM deploys AI that runs on your hardware, searches your documents, and never sends a byte to OpenAI, Google, or Microsoft. We're a Melbourne consultancy that installs self-hosted language models and private document search for law firms, healthcare providers, and financial services teams — the kind of firms where pasting client data into ChatGPT isn't an option.
Run models like Llama 3, Mistral, or Gemma 4 on your own server. No API calls. No cloud dependency. Your IT team controls the infrastructure.
Ask a question across thousands of internal documents and get an answer with source citations — all processed on your network.
We handle model selection, RAG configuration, access controls, and ongoing maintenance so your team can focus on the work.
QUERY: "Search internal policy and financial records from 2014–2024"
PUBLIC AI TOOLS ARE NOT DESIGNED FOR EVERY RISK PROFILE
When your staff paste client briefs, patient records, or financial data into ChatGPT or Copilot, that information lands on servers you don't control — often overseas. Here's what that means in practice.
EXTERNAL DATA EXPOSURE
A junior lawyer pastes a privileged settlement offer into ChatGPT to summarise it. That data now sits on OpenAI's servers in the US. Under APP 8 of the Privacy Act 1988, the firm may have just triggered a cross-border disclosure issue.
LIMITED ENVIRONMENT CONTROL
With ChatGPT or Copilot, you don't choose where the model runs, how long prompts are retained, or who else's data trains the next version. Your IT team has no access to server logs, no ability to enforce role-based restrictions, and no kill switch.
POLICY AND COMPLIANCE PRESSURE
APRA CPS 234 requires financial firms to manage information security risks for third-party services. The My Health Records Act restricts how patient data flows. Legal privilege can be waived by voluntary disclosure. Using public AI with this data creates compliance headaches that don't exist with on-premises alternatives.
WEAKER AUDITABILITY
When ten people use personal ChatGPT accounts, there's no central log of what was queried, what documents were uploaded, or what advice the AI returned. A self-hosted system gives you full audit trails — who asked what, when, and which documents were cited in the response.
PRIVATE AI SOLUTIONS
Three core services. Each keeps your data off public AI platforms and inside your own infrastructure.
LOCAL LLM DEPLOYMENT
We install open-source models like Llama 3 or Gemma 4 on your server. Summarise documents, analyse contracts, answer questions — all on-premises.
Learn more → searchPRIVATE DOCUMENT SEARCH
RAG-powered semantic search across your internal documents. Ask a question in plain English, get an answer with source citations — nothing leaves your network.
Learn more → support_agentONGOING SUPPORT
Monthly retrieval audits, document corpus updates, model upgrades, and troubleshooting. We keep the system sharp as your needs evolve.
Learn more →BUILT FOR FIRMS WHERE DATA
HANDLING IS A SERIOUS CONSTRAINT
We work with three types of firms. All of them handle data that cannot touch a public AI platform — and all of them have staff who want AI tools yesterday.
LEGAL
PRIVILEGED DATA
For law firms handling privileged client material, matter files, internal precedents, and confidential working documents.
HEALTHCARE
RESTRICTED RECORDS
For clinics, private hospitals, and healthcare operators managing patient sensitive information and operational documentation.
FINANCIAL SERVICES
REGULATED WORKFLOWS
For accounting firms, wealth firms, and advisory teams working with sensitive financial records and regulated processes.
PRIVATE AI DEPLOYMENT PROCESS
Most deployments take 4 to 8 weeks from first conversation to working system. Here's what that looks like.
Assess
We review your documents, infrastructure, compliance requirements, and what your team actually needs AI to do. We've seen firms with 500 documents and firms with 500,000 — the process adapts.
Design
We pick the right model for your workload, design the retrieval pipeline, map out role-based access controls, and plan the hardware setup — whether that's an existing server or new kit.
Build
We install the language model, ingest and index your documents, configure the search and question-answering workflows, and lock down access permissions.
Validate
We test with real queries from your team: does the system find the right document? Is the summary accurate? Does it fit into how people actually work? We refine until it does.
Support
AI systems need maintenance — new documents to ingest, models to upgrade, retrieval to tune. We handle that monthly so the system stays useful as your organisation grows.
WHAT A DEPLOYMENT LOOKS LIKE
Mid-tier commercial law firm, Melbourne CBD
PROBLEMThree practice groups using personal ChatGPT accounts for matter summarisation. Compliance team flagged it as an uncontrolled privilege risk.
On-premises AI system on existing server infrastructure. Indexed 20,000+ matter files with ethical walls between practice groups. RAG-powered search with source citations.
TIMELINE4 weeks from first conversation to production
60% staff adoption in the first month. Compliance concern shifted from "how do we stop AI usage" to "how do we expand this to more document types."
No client data has left the firm's network. The system now handles daily queries across all three practice groups.
Details adjusted for confidentiality
COMMON QUESTIONS
What is AIRGAP LLM?
AIRGAP LLM is a Melbourne consultancy that installs and runs AI systems on your own hardware — not in the cloud. We work with law firms, healthcare providers, and financial services firms that handle data too sensitive for ChatGPT, Copilot, or other public AI platforms. Our deployments cover on-premises language models, private document search using RAG (retrieval-augmented generation), and ongoing system support. Every component stays within your network, so nothing reaches an external server.
What is a local LLM deployment?
A local LLM deployment means running an open-source language model — such as Llama 3, Mistral, or Gemma 4 — on a server inside your office or data centre. No queries, documents, or AI responses pass through external platforms like OpenAI or Google. For example, a partner at a Melbourne litigation firm can ask the system to summarise a case from 2019, and the answer is generated entirely on hardware the firm controls. This matters for organisations subject to the Privacy Act 1988, where cross-border data transfers under APP 8 require careful management.
Who needs private AI deployment?
Any organisation where staff are tempted to use ChatGPT but compliance says no. That typically includes law firms protecting legal privilege, healthcare providers bound by the My Health Records Act 2012, and financial services firms navigating APRA CPS 234. We also work with accounting firms, wealth managers, and government-adjacent teams in Melbourne. If your data is sensitive enough that pasting it into a public AI tool would be a policy breach, self-hosted AI is the practical alternative.
Where is AIRGAP LLM based?
We're based in Cremorne, Melbourne (Gwynne St, Cremorne VIC 3121) — right in the middle of Melbourne's technology precinct, a few minutes from Richmond station. Being Melbourne-based means we can be on-site for deployments, training sessions, and support across the metro area and greater Victoria.
What industries does AIRGAP LLM serve?
We focus on three industries where data sensitivity makes public AI adoption risky. Legal — law firms handling privileged client material, matter files, and confidential working documents. Healthcare — clinics and private hospitals managing patient records under the My Health Records Act. Financial services — accounting firms, wealth managers, and advisory teams subject to APRA prudential standards. Each industry has its own regulatory landscape, and we tailor every deployment accordingly.
How does local LLM deployment protect sensitive data?
The short answer: your data never leaves your building. Every prompt, every document chunk, and every AI-generated response stays on hardware you own and control. There are no API calls to OpenAI, Google, or Microsoft. This makes compliance with the Privacy Act 1988, APRA CPS 234, and professional confidentiality obligations significantly simpler because there is no third-party data processor to assess, audit, or worry about. It also means you control retention, access logs, and who can query what — down to the practice group or department level.
Want to See How This Works for Your Firm?
We'll walk you through a deployment that fits your setup — your documents, your infrastructure, your compliance requirements. No sales pitch.
Request a Consultation →Or email us directly at hello@airgapllm.com.au