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Insights

Insights

Technical writing on private AI, retrieval, data and automation for regulated Australian businesses. Start with a field guide, or browse every article below.

Field guides

Glossary

12 articles

Private AI and self-hosted LLMs

How to run language models inside your own cloud account or data centre in Australia: architecture, hardware, models, retrieval, security and cost.

All articles

Private AI

Air-gapped LLM deployments in Australia

What a true air gap needs for an LLM stack: weight transfer and verification, offline mirrors, no telemetry, local evaluation, licences, and who needs it.

Private AI

Amazon Bedrock and Azure OpenAI in Australian regions

Which models run in Sydney, Melbourne and Australia East as of October 2026, how routing options move prompts, and what each provider retains.

Private AI

Open-weight LLMs compared: licences, size and fit in 2026

How to pick an open-weight LLM for private use in Australia, by evaluation set, licence terms, GPU footprint, context, languages and tool calling.

Private AI

Data sovereignty vs data residency for AI in Australia

Residency, sovereignty and operational control are three separate guarantees. How APP 8, APP 11, CPS 234, CPS 230 and the HCF apply to an AI system.

Private AI

When to fine-tune a model and when to use retrieval

When to use retrieval, when to fine-tune and in what order, with LoRA and QLoRA explained, upkeep costs, evaluation baselines and a decision checklist.

Private AI

Buying, leasing or renting GPUs for private AI in Australia

Buy, lease or rent GPUs for private AI in Australia. Which GPU instances each cloud offers locally, the break-even utilisation and the contract risks.

Private AI

Securing a self-hosted LLM: controls mapped to Essential Eight

Layered controls for a private LLM stack, from network and identity to model files, prompt injection, output checks and patching, mapped to E8 and CPS 234.

Private AI

LLM evaluation and tracing on your own servers

Build an evaluation set from past cases, gate releases in CI, trace with OpenTelemetry into self-hosted Langfuse and Grafana, and log for audit.

Private AI

Self-hosted LLM or API: a break-even model

A transparent cost model for self-hosted LLMs against a per-token API in an Australian region, with a worked break-even and the costs teams leave out.

Private AI

On-prem, VPC or hybrid: where a private LLM should run

How on-prem, VPC and hybrid LLM deployments differ at each layer, from vLLM serving to egress rules, and which one fits an Australian regulated business.

Private AI

Private RAG stack: parsing, hybrid search and permissions

A reference stack for RAG on your own servers. Parsing, chunking, embeddings, pgvector or Qdrant or Elasticsearch, reranking, ACLs and page citations.

Private AI

How much GPU memory an LLM needs: a worked sizing guide

Work out GPU memory for model weights and KV cache, estimate throughput from memory bandwidth, and pick a GPU tier for on-prem LLM inference.

Strategy

Why your AI pilot stalled

Most stalled AI programs die between the demo and production, for reasons that have nothing to do with model capability. Five reasons account for almost all of it.

Knowledge

A company brain is not a chatbot

The chat interface is the least interesting part of an internal knowledge system. The parts that decide whether it works are retrieval quality, permissions, freshness and who reads the bad answers.

Automation

Where to put the human in an automated workflow

Place review points by the cost of an error. A simple framework for deciding which steps run unattended and which keep a person forever.

Field notes

Nine questions that change a vendor call

Field notes on evaluating AI platforms. Most vendor decks are true and still misleading. These questions surface the gap.

Next step

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