LOCAL LLM DEPLOYMENT, BUILT FOR CONTROL
Local LLM deployment runs a large language model entirely within your organisation's controlled infrastructure — on-premise servers or private cloud — so that no queries, documents, or responses pass through external platforms. AIRGAP LLM deploys private AI systems for law firms, healthcare providers, and financial services organisations in Melbourne, ensuring compliance with the Privacy Act 1988 and industry-specific regulations.
We help organisations deploy private AI systems for internal use, with stronger control over infrastructure, document access, and sensitive workflows. This is suited to firms that want the benefits of AI without defaulting to public-platform usage for internal knowledge tasks.
LOCAL DEPLOYMENT OPTIONS
Deploy within an environment aligned to your internal requirements, whether that means on-premise, isolated infrastructure, or a tightly controlled private setup.
PRIVATE INTERNAL OPERATIONS
Keep document search, retrieval, and question-answering within a controlled workflow designed around your organisation's risk profile.
SECURE DOCUMENT ACCESS
Apply structured access around internal records, policies, knowledge bases, and sensitive operational documents.
LOCAL LLM VS CLOUD AI
| Factor | Local LLM | Cloud AI (ChatGPT, Copilot) |
|---|---|---|
| Data location | On-premise / private infrastructure | External servers, often overseas |
| Privacy Act 1988 compliance | Full control over data handling | Requires detailed assessment of APP 8 |
| Auditability | Full logging and access control | Limited to provider's audit features |
| Customisation | Fine-tuned for your documents and workflows | General-purpose, limited customisation |
USE CASES
"For organisations handling privileged client material, the question is not whether AI is useful — it is whether the deployment model respects the same confidentiality boundaries the firm already maintains. Local LLM deployment is the only approach that keeps privileged information within the firm's control."
FREQUENTLY ASKED QUESTIONS
What is local LLM deployment?
Local LLM deployment is the practice of running large language models entirely within your organisation's own infrastructure — on-premise servers or private cloud — so that no queries, documents, or responses pass through external third-party platforms. AIRGAP LLM specialises in this approach for Melbourne-based organisations subject to the Privacy Act 1988 and industry-specific regulations.
What hardware is required for local LLM deployment?
Hardware requirements depend on the model size and use case. AIRGAP LLM assesses your specific needs during the initial consultation and recommends infrastructure that balances performance, cost, and security requirements. Options range from GPU-equipped workstations to dedicated server infrastructure.
How long does a local LLM deployment take?
A typical deployment follows AIRGAP LLM's five-step process (Assess, Design, Build, Validate, Support) and takes 4-8 weeks from initial consultation to production readiness, depending on the complexity of your document corpus and infrastructure requirements.
Can local LLMs match the quality of cloud AI services?
Modern open-source LLMs deliver strong performance for enterprise tasks like document summarisation, question-answering, and analysis. AIRGAP LLM selects and configures models optimised for your specific use case, ensuring high-quality outputs within your controlled environment.
Ready to Deploy Private AI?
Get in touch to discuss how AIRGAP LLM can help your organisation deploy private AI systems with full data control.
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