AI & Machine Learning
We design, build and operate AI systems that answer accurately, cite their sources, stay within budget and keep improving once real users arrive.
Overview
Most organisations no longer need convincing that AI is useful. The hard part is getting a prototype to behave reliably on real data, inside real security boundaries, at a cost the business can live with.
Our AI engineering practice treats models as one component in a larger system. We spend as much effort on retrieval quality, evaluation, guardrails and observability as on the model itself, because that is where production AI succeeds or fails.
Business challenges
A notebook demo works on twenty hand-picked questions, then falls apart on the long tail of real user queries.
Without grounding and citations, teams cannot trust outputs enough to put them in front of customers or regulators.
Token spend and response times grow faster than usage, and no one can explain which prompts or features are driving them.
Prompt changes ship on gut feel because there is no evaluation set, no regression testing and no quality baseline.
Our solutions
Customer-facing and internal applications built on large language models, designed around a specific job to be done.
RAG pipelines that ground answers in your own content with permissions respected and sources cited.
Predictive and perception models where classical ML or fine-tuned models outperform general-purpose LLMs.
The operational layer that keeps models accurate, observable and affordable after launch.
Capabilities
Technologies
Technology choices are made per project based on your environment, skills and constraints.
Industry applications
Engagement approach
We define the decision or task the AI supports, the quality bar and how success will be measured.
Before building, we assemble a representative test set so every iteration is scored against reality.
Retrieval, prompts, guardrails and integrations are developed together and tested end to end.
Monitoring, feedback loops and scheduled re-evaluation keep quality from drifting.
It depends on the task, data residency, latency and cost targets. We usually benchmark two or three candidate models against your evaluation set and recommend based on measured results rather than brand preference. Architectures are kept model-agnostic so you can switch later.
Yes. Major cloud providers offer model hosting in Australian regions, and open-weight models can run inside your own environment. We design around your residency and privacy obligations from the start.
Through grounded retrieval, constrained output formats, citation requirements, refusal behaviour for out-of-scope questions and automated evaluation that flags unsupported claims before release.
Not for most generative AI use cases. Retrieval-based systems work with your existing documents. Custom predictive models do need historical data, and we assess its quality during discovery.
Start a conversation
Tell us where it stands. We will help you work out what it takes to run it reliably.