AI Engineering — Australia
AI engineering for Australian businesses — from prototype to production.
Australia's AI adoption is among the highest in the Asia-Pacific — driven by mining and resources, financial services, and a growing healthtech sector. Most companies have the use case and the data. The gap is production engineering: evaluation frameworks, cost routing, and the reliability that separates a demo from something you can ship.
What we deliver
- LLM integration for Australian products and enterprise platforms
- RAG pipelines for financial, healthcare, and resources sector documents
- Multi-agent workflows for operations and process automation
- Edge AI and IoT integration for mining, agritech, and logistics
- Privacy Act-compliant data architecture for AI systems
- Full-stack AI product development from architecture to production launch
The Australian AI market
Australia's enterprise AI adoption sits at approximately 35–40% — above most EU markets and comparable to the United States. BHP, Rio Tinto, and Fortescue have operated production AI for predictive maintenance and autonomous systems since the mid-2010s, creating an engineering culture that understands what production AI actually requires.
Financial services AI is growing fast, with ASIC and APRA establishing expectations around explainability and monitoring that parallel FCA guidance. Healthcare AI investment is accelerating, with Privacy Act compliance and potential SaMD classification under TGA governing what can be deployed and how.
The constraint is consistent across sectors: production AI engineering capacity. Companies with the right use case, the right data, and the budget are stuck between a working prototype and a system they can actually ship at scale.
How we work
Scoped assessment
We look at your use case, your data, and your regulatory environment — APRA, ASIC, Privacy Act, or TGA SaMD. We tell you what the architecture needs to look like before we write a line of code.
Evaluation framework first
Before building, we define how we'll know if the system is working. A representative query set, scoring criteria, and the baseline we're improving against.
Production build
Retrieval architecture, cost routing, streaming, monitoring, fallback handling. The engineering that separates a demo from a system that handles real load reliably.
Handover and continuity
Your team can run and extend it. We document the architecture, train whoever is operating it, and stay available as the system grows.
Related insights
AI Engineering — Australia
Ready to ship AI in Australia?
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