Platforms that hold up
Warehouse and lakehouse architecture, pipelines, modelling and governance. Assessments of what you already have, and the migration path off it when that is the honest answer.
Data / AI / Engineering
Hard problems in data, AI and engineering — solved end to end.
A boutique team for enterprises that need the work done properly, not demonstrated. We name the problem before the method, and the result before the technology.
Our practice
01 · Data
02 · AI
03 · Engineering
Three capabilities, one team
Most engagements draw on all three. We staff small, senior and hands-on — the people who scope the work are the people who do it.
Systems, not demos
Applied machine learning and LLM systems taken to production: evaluation harnesses, retrieval, guardrails, cost and latency budgets, and the monitoring that keeps them trustworthy.
Built to be handed over
Backend and cloud engineering, integration, and the delivery practices around them — tests, CI, observability, documentation. We optimise for the team that inherits the code.
Work
Assessment
Delivery
How we engage
Fixed scope where the problem is clear, and a short paid assessment where it is not. No open-ended staff augmentation.
Two to four weeks
A blunt read on the current state — architecture, data quality, cost, risk — with a costed plan and a recommendation you can take to a board. Useful on its own, whether or not we build it.
Scoped build
We build the thing, in your stack, alongside your people, with a defined end date and a handover that lands: runbooks, tests, and a team that can operate it without us.
Approach
Evidence first
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01
Name the problem
Before any architecture, we agree in writing what is actually wrong and how we will know it is fixed. Most failed programmes skip this step.
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02
Measure before you move
Baselines for cost, latency, quality and effort. Decisions get made against numbers, not preference or vendor material.
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03
Ship something small early
A narrow slice in production beats a broad slice in a slide deck. It surfaces the integration problems while they are still cheap.
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04
Leave it maintainable
Plain code, current documentation, tests that mean something. The measure of the engagement is how your team goes six months after we leave.
Contact
Start a conversation
Tell us the problem in a paragraph — the messier the better. We will tell you honestly whether it is work we should take.
hello@arsconsulta.com.au