Past the pilot.
Into production.
Most organisations we meet have already built an AI demo. Very few have one in production with evaluation, guardrails, cost controls and an owner. We close that gap — on AWS, with your data staying in Australia.
AI is an operations problem
wearing a research costume
The model is rarely the hard part. The hard part is retrieval quality, data access control, evaluation you can defend, a cost per query that scales, and a clear answer to "who is accountable when it is wrong?" We build for those first.
Where AI has actually paid for itself
Patterns we have delivered more than once, with the economics to justify a second phase.
Document intelligence
Contracts, claims, referrals, tenders and compliance packs — extracted, classified, summarised and routed, with the source page cited for every assertion.
Knowledge assistants
Grounded RAG over policy, product and procedure content for contact centres, advisers and field staff — with permissions honoured at retrieval time, not after.
Forecasting & demand
Inventory, staffing, cash and capacity forecasting on SageMaker, with backtesting your finance team can interrogate line by line.
Risk & anomaly detection
Fraud signals, transaction monitoring and operational anomalies, with explainability attached so an analyst can act on the alert rather than argue with it.
Agentic workflow automation
Multi-step processes — reconciliation, onboarding checks, ticket triage — where the agent calls your systems through tools, inside an audit trail.
Engineering acceleration
Assisted development, legacy code comprehension and automated test generation — rolled out with measurement, not vibes, so you know whether it worked.
From opportunity map to production in one quarter
Deliberately short cycles with a kill switch at every stage. If the value is not there, you find out in week four — not after nine months of build.
Start with a discovery workshopWeeks 1–2 · Opportunity mapping
Workshops with the people doing the work. Each candidate use case scored on value, data readiness, risk and feasibility, then ranked into a portfolio.
Weeks 3–4 · Data & feasibility
Can we reach the data lawfully, at quality, in Sydney? We prove retrieval quality on a real sample before anyone commits to a build.
Weeks 5–9 · Proof of value
A working system for real users on a narrow scope, with the evaluation harness built alongside it — not bolted on at the end.
Weeks 10–13 · Production hardening
Guardrails, red-teaming, observability, cost ceilings, human-in-the-loop design, runbooks and the governance sign-off your risk function requires.
Ongoing · Operate & improve
Regression suites on every model or prompt change, drift monitoring, spend tracking, and a quarterly review of whether it is still earning its keep.
The governance layer, built in from week one
Aligned to Australia's AI Ethics Principles and the voluntary AI Safety Standard, and designed to satisfy the risk questions your board will ask before go-live.
Data boundaries
Region-pinned inference, no training on your data, per-tenant isolation and retrieval that enforces existing permissions rather than re-implementing them.
Evaluation & gates
Golden datasets, groundedness and citation scoring, adversarial suites, and deployment gates that block a release when quality regresses.
Accountability
Model cards, decision logs, escalation paths and a named human owner per system — the paperwork that turns a demo into something an auditor accepts.
SageMaker
Ready
Built on AWS AI services
Amazon Bedrock for foundation models and guardrails, Knowledge Bases and OpenSearch Serverless for retrieval, SageMaker for custom models and MLOps, and Step Functions for orchestration — inside the same account, network and control plane as the rest of your platform.
The ones that come up in every board paper
No. We build on Amazon Bedrock, where your inputs and outputs are not used to train the underlying foundation models and are not shared with model providers. We document the data flow explicitly so your privacy team can review it.
Three ways: retrieval designed so the answer exists in the context, citation enforcement so unsupported claims are rejected, and an evaluation suite that scores groundedness on every change. For high-consequence decisions we design human review into the workflow rather than relying on model behaviour alone.
We model cost per interaction during the proof of value and set hard spend ceilings before production. Caching, prompt compression and routing simpler queries to smaller models typically cut inference cost by half or more without a measurable quality loss.
Yes. In practice a foundation and data-access workstream runs alongside the AI work — which is exactly why our cloud and AI practices sit in the same delivery team rather than in separate business units.
Bring us your stalled pilot
Or your blank page. Either way, the first session is a working one: we leave with a ranked list of use cases and an honest read on which are worth funding.