Most AI tools send your data to someone else's server. That's fine for some things. For sensitive client data, legal documents, patient records, or financial information, it's a risk you don't need to take.
When staff paste client data into public AI tools, your organisation may be in breach of APP 8 of the Privacy Act 1988 — triggering cross-border disclosure issues without realising it.
With cloud AI services, 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 and no kill switch.
Regulated industries face specific obligations: APRA CPS 234 for financial services, My Health Records Act for healthcare, and legal privilege protections that can be waived by voluntary disclosure to third-party platforms.
Personal AI accounts leave no central log of who asked what, when, or which documents were cited. A self-hosted system gives you full audit trails — every query, every response, every document accessed.
We install open-source language models — Llama 3, Mistral, Gemma, and others — on your own server. Summarise documents, analyse contracts, answer questions. Everything runs on-premises.
RAG-powered semantic search across your internal documents. Ask questions in plain English, get answers with source citations — nothing leaves your network.
Monthly retrieval audits, document corpus updates, model upgrades, and troubleshooting. We keep your private AI running smoothly.
We review your documents, infrastructure, and compliance requirements. Whether you have 500 documents or 500,000 — we scope it properly.
We select the right model, design the retrieval pipeline, map role-based access controls, and plan your hardware setup.
We install the model, ingest and index your documents, configure search and QA workflows, and lock down permissions.
We test with real queries from your team and refine until the system is accurate, fast, and actually useful in daily work.
Monthly maintenance: new documents added, models upgraded, retrieval quality tuned. We keep things running.
We deploy private AI for organisations where data security isn't optional.
Privileged client material, matter files, internal precedents. Legal privilege depends on confidentiality — public AI tools can waive it.
Patient records, clinical policies, referral letters. The My Health Records Act sets clear rules about how data can be handled.
Client portfolios, compliance documents, market research. APRA CPS 234 demands you manage third-party information security risk.
| Detail | Value |
|---|---|
| Industry | Mid-tier commercial law firm, Melbourne CBD |
| Problem | Three practice groups using personal ChatGPT accounts for matter summarisation; compliance flagged uncontrolled privilege risk |
| Deployment | On-premises Llama 3 on existing server; indexed 20,000+ matter files with ethical walls; RAG-powered search with source citations |
| Timeline | 4 weeks from first conversation to production |
| Outcome | 60% staff adoption in first month; compliance shifted from "stop AI" to "expand to more document types" |
| Key result | No client data left the firm's network |
Details adjusted for confidentiality.
| Detail | Value |
|---|---|
| Industry | Private healthcare clinic group, Melbourne eastern suburbs |
| Problem | Admin staff using ChatGPT with patient intake forms and referral letters; breach of My Health Records Act requirements |
| Deployment | On-premises Llama 3 with RAG pipeline across 8,000 patient policy documents and intake form templates |
| Timeline | 6 weeks |
| Outcome | 85% staff adoption within first month; $7,200/month saved in admin processing time; zero compliance incidents |
| Key result | No patient data left the clinic network |
Details adjusted for confidentiality.
| Detail | Value |
|---|---|
| Industry | Boutique wealth management firm, Melbourne |
| Problem | Advisors pasting client portfolio summaries into public AI tools for research queries; APRA CPS 234 third-party risk exposure |
| Deployment | On-premises Mistral model on dedicated GPU server; RAG pipeline across client portfolio data, compliance policies, and market research |
| Timeline | 8 weeks |
| Outcome | 90% advisor adoption; client query response time cut from 45 minutes to under 5; compliance team signed off on full production use |
| Key result | $180K annual savings in avoided cloud API costs and compliance remediation |
Details adjusted for confidentiality.
Running an open-source language model (e.g., Llama 3, Mistral, Gemma) on a server inside your office or data centre. No queries or responses pass through external platforms — everything stays on your network.
Any organisation where staff want to use AI but compliance says no. Law firms (legal privilege), healthcare (patient data), financial services (APRA), accounting, wealth management — or any business that simply wants to own its AI, not rent it.
It depends on your document volume and user count. For most mid-size deployments, a single GPU server handles the workload. We assess your specific needs during the scoping phase and recommend hardware that fits your budget.
Typically 4–8 weeks from first conversation to production, depending on document volume and infrastructure. Smaller deployments can be faster. We'll give you a clear timeline after the assessment.
We'll walk through your setup — your documents, your infrastructure, your compliance requirements. No sales pitch.
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