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Services / Data and AI Platforms
Data and Applied AI
A single, trusted
view of the truth
We architect and deliver the data platforms that give an organisation one trusted view of itself, with the governance, sovereignty, and automation that modern reporting demands.
Service Outline
What this service delivers
Most organisations do not lack data. They lack a trusted, governed, current view of it. We architect and deliver the data platforms that give an organisation one version of the truth: fragmented sources consolidated into a governed lakehouse, statutory and executive reporting automated, and the foundations laid for applied AI that actually works. Our practice is grounded in DAMA-DMBOK disciplines (governance, quality, lineage, metadata, and stewardship), and designed for Australian conditions: sovereignty, residency, IRAP and ISM obligations, and privacy compliance are architected in from the outset. Because in the environments we work in, where the data lives and how it is governed matters as much as what it says.
The Precision Approach
How we run this work
Trust before scale.
Data quality, lineage, and ownership are established for the domains that matter before the platform grows, because a big lake of doubtful data is a liability.
Govern by the book.
Stewardship, cataloguing, metadata, and quality management follow DAMA-DMBOK discipline, applied with pragmatism rather than ceremony.
Sovereign by design.
Residency, classification, IRAP-assessed hosting, and ISM control mapping are architecture inputs from day one in government and regulated work.
Build enduring data products.
Data domains are delivered as owned products with consumers, service standards, and a funded backlog, so the platform keeps earning after we leave.
Automate the grind.
Ingestion, quality checks, reconciliation, and reporting pipelines run themselves, with AI-assisted anomaly detection watching the flows.
AI-ready from the foundation.
Feature pipelines, model-ready data contracts, and MLOps foundations are designed in, so applied AI lands on solid ground.
How We Can Help
Structured by discipline. Shaped to your programme.
These are representative artefacts from our delivery playbooks, tailored, added to, or trimmed to fit each client’s governance model and what the programme actually needs.
Set the strategy 3 shown
- Enterprise data strategy aligned to business value cases (DAMA-DMBOK informed)
- Data platform target architecture: lakehouse, streaming, semantic, consumption
- Platform selection and build-versus-buy evaluation with cost modelling
Govern the data 5 shown
- Data governance operating model: councils, stewards, owners, decision rights
- Data policy suite: classification, retention, access, quality, ethics
- Enterprise data catalogue with automated lineage capture
- Data quality framework with rules, scorecards, and remediation workflow
- Master and reference data management design for the critical domains
Build the pipelines 4 shown
- Source system consolidation plan with ingestion patterns per source
- Automated ingestion and transformation pipelines with orchestration and tests
- Statutory and executive reporting automation with reconciliation controls
- Semantic layer and governed self-service analytics design
Sovereign and secure 5 shown
- Sovereignty and residency assessment with hosting recommendations
- ISM control mapping and IRAP readiness pack for government workloads
- Privacy impact assessment and APP compliance mapping
- Security architecture: identity, encryption, segmentation, monitoring
- Data sharing and interoperability framework, including DATA Scheme considerations for government
Lay the AI foundations 2 shown
- AI-ready data foundations: feature pipelines, data contracts, versioning
- MLOps platform foundations: experiment tracking, registry, deployment paths
Run it and prove it 4 shown
- Cost and consumption governance for the platform (FinOps for data)
- Data literacy and stewardship capability uplift programme
- Platform operations handbook and support model
- Benefits tracking: reporting cycle time, quality incidents, decision latency
This is a representative set. If what you need is missing here, ask us.
The Precision Difference
Why boards choose us for this work
One version of the truth
Fragmented sources are consolidated into a governed lakehouse, with quality, lineage, and ownership established for the domains that matter before the platform grows. A large store of doubtful data is a liability rather than an asset, which is why the order matters.
Sovereign by design
Residency, classification, IRAP-assessed hosting, and ISM control mapping are architecture inputs from day one. In the environments we work in, where the data lives and how it is governed matters as much as what it says.
Governed by the book, run as a product
DAMA-DMBOK discipline applied with pragmatism rather than ceremony, and data domains delivered as owned products with consumers, service standards, and a funded backlog. The platform keeps earning after we leave.
Senior only. Fiercely independent. 94.3 per cent on-budget across AU$2.3 billion governed since 1996.
What we do
Data and AI Platforms
Few organisations are short of data. What they lack is a trusted, governed, and current view of it. We consolidate fragmented sources into a coherent platform, automate the reporting that used to take weeks, and build the foundation that applied AI depends on.
We design for auditability and sovereignty from the outset, because in the environments we work in, where the data lives and how it is governed is as important as what it says.
Data platform architecture
A governed, sovereign-ready data lakehouse built for scale.
Source consolidation
Many legacy sources brought into one coherent, trusted view.
Automated reporting pipelines
Statutory and executive reporting that runs itself.
Data governance
The lineage, quality, and controls that make the data trustworthy.
AI-ready foundations
The platform on which applied AI actually works.
Impact and Outcomes
Spotlighting some of our Data and AI Platforms impact
Ready to Engage?
Talk to us about
Data and AI Platforms
Initial conversations are confidential and without obligation.
If your executive reporting takes weeks to reconcile, or your AI ambitions rest on data nobody fully trusts, the platform conversation needs to come first.