Maintain greater control over your data and AI infrastructure.
For many organisations, the question is not whether AI could help, but whether they can use it without handing sensitive data to a third party or locking themselves into someone else’s pricing.
Open-weight models have changed that answer. The leading models — from Qwen and DeepSeek to Llama and Mistral — can now run inside your own cloud account, on your own hardware or in an Australian data centre, at quality that is highly competitive for a wide range of real tasks.
Getting there takes real engineering: choosing the right model for each task, deploying it securely, tuning it on your domain, serving it efficiently and keeping it current as better models appear. We design private AI solutions around your security and privacy requirements, and combine them with commercial platforms wherever that serves you better.
For sensitive or regulated workloads, running the model inside your own environment removes a whole class of data-sovereignty and third-party risk questions.
The best model on a public leaderboard is often not the best model for your workload. We test on yours, and include commercial options where they are the better fit.
A well-tuned smaller model frequently outperforms a large general one on a narrow task, at a fraction of the running cost.
Most production setups route between models — private models for sensitive or high-volume work, commercial models where they add the most. The goal is the right control and cost for each task.
Security, privacy, sovereignty, performance and cost requirements agreed up front, because they drive every later decision.
Open-weight and commercial models evaluated on your tasks for quality, latency and cost.
Hosting, network isolation, access control and serving architecture designed to your security posture.
Fine-tuning where it pays off, plus guardrails, evaluation suites and fallback behaviour.
Monitoring for quality, drift and cost — and periodic re-benchmarking, because open models improve quickly.
Private AI engagements usually start with a short benchmark, so the decision is made on measured quality, security and cost.
For a large share of business workloads — classification, extraction, summarisation, question answering over documents, coding assistance — leading open-weight models are now highly competitive. For the hardest reasoning tasks a commercial model can still be worth it. We measure rather than assume.
Yes. Self-hosting in an Australian cloud region or on your own infrastructure keeps data onshore, which is often the simplest answer to data-sovereignty requirements.
Often, particularly at volume or with a well-tuned smaller model, but it depends on usage, model size and hosting. The benchmark phase gives you real numbers for your workload before you commit.
Open-weight models are downloaded and run entirely inside the environment you choose, so no data is sent to the model developer. We still assess each model against your risk and compliance requirements and recommend alternatives where policy requires.
No. Many solutions combine both — private models for sensitive or high-volume work, commercial models where they add the most value.
Tell us about your business challenges, operational bottlenecks or ideas for AI. We will help you explore what is possible and identify a practical path forward.
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