Enterprise AI for Australian business
We design and operate LLM platforms, retrieval systems, and AI agents that meet enterprise security, sovereignty, and reliability standards — from proof of concept to production.
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Vendor-neutral. We build on:
What we build
01 / LLM OPS
Evaluation harnesses, prompt versioning, observability, cost controls, model gateways. Whatever takes your AI from prototype to operations.
Every release passes an offline eval suite and a regression gate before it reaches production.
02 / RAG
Document ingestion, embedding pipelines, hybrid retrieval, grounded answers with citations. Built for your data, your privacy boundary.
Grounded answers your compliance team can audit — every claim traceable to a cited source.
03 / AGENTS
Tool-using agents for internal operations, customer service, and analyst-grade research. Designed with eval gates and human-in-the-loop.
Eval-gated and human-in-the-loop, scoped to refuse rather than guess.
How we work
A low-risk on-ramp: you commit to two weeks, not a transformation programme.
i.
A fixed-price, two-week discovery. We scope the problem, your data, and the success criteria — and you leave with an architecture, a quote, and working artifacts. If it won't work, we tell you, and you keep what we've built.
ii.
Fixed scope, fixed price. We instrument the system for evaluation as we build it — and the source code is yours to run, with or without us.
iii.
Optional ongoing operations: model upgrades, eval-drift monitoring, and incident response — only if you want us past handover.
Why us
An AU-based team on AU business hours. Your data stays inside your sovereign boundary — we don't move it offshore.
Evals, tracing, and promotion gates from day one. We measure a system before we ship it — and again after.
Anthropic, OpenAI, AWS Bedrock, Azure, or self-hosted — chosen on merit for your workload, never to fill a partner quota.
No bench, no offshore handoff. The people who scope your build are the people who build it.
Your data stays in your environment and we don't train on it. Every system ships with the access controls and audit trails your risk team expects.
Who we work with
We speak the compliance language of the industries we serve — not just the model APIs.
APRA CPS 230 and CPS 234. Operational-risk controls, incident-notification timeframes, and grounded answers your risk and compliance teams can audit.
ISM- and IRAP-aligned deployments with data-classification handling — AI for agencies that can't send data offshore.
Privacy Act and My Health Record obligations. Citable retrieval over clinical and policy documents, with a refusal path instead of a guess.
SOCI critical-infrastructure obligations. Operational AI over the document-heavy, safety-critical workflows these sectors run on.
See it work
A recorded walkthrough of a retrieval system answering a regulated question — with citations you can open, a visible trace, and an honest refusal when the answer isn't in the source.
Recorded illustrative demo — runs entirely in your browser, no live model or backend
Analyst
What are our incident-notification timeframes under CPS 230?
Assistant
Under CPS 230, an operational risk incident must be notified to APRA as soon as possible, and no later than 72 hours, after you become aware of it — where the incident is likely to have a material financial impact or a material impact on your ability to maintain critical operations[1]. “Critical operations” here means processes that, if disrupted beyond tolerance levels, would have a material adverse impact on your depositors, policyholders or other customers, or on your role in the financial system[2].
Analyst
And what's the financial penalty if we miss the 72-hour deadline?
Assistant — abstained
That isn't stated in the provided documents. CPS 230 sets the 72-hour notification obligation, but the excerpts here don't specify any financial penalty for late notification — so I won't guess. Escalating to a human, with the relevant clause attached.
Pipeline
Evals
Insights
Vendor-neutral platform
A practical decision framework for where your model actually runs when data residency and sovereignty are hard constraints — and how to keep the choice reversible.
14 June 2026 · 2 min read
RAG & retrieval
Grounding, inline citations, retrieval evals, and a refusal path — the engineering that makes a retrieval system answer to an auditor, not just a demo.
12 June 2026 · 3 min read
LLMOps & reliability
How we stop 'looked good in the demo' from reaching production — the offline eval suite, the regression gate, and the tracing we wire in from day one.
9 June 2026 · 3 min read
Questions
Email us a sentence on what you're trying to build. We reply within two business days.
Scope your AI buildor email [email protected]