We get your Microsoft Fabric foundation right: resilient architecture, governed semantic models, operational governance. So the analytics your organisation relies on is dependable today - and ready for AI when the tooling is ready for you.
Organisations have spent a decade building data platforms. Warehouses, lakes, pipelines, dashboards - mature, governed, trusted. Then the board asks why the AI investment isn't paying off.
The answer is rarely the model. It's that the data underneath was built for humans to query, not for machines to reason over.
A complete, described, governed semantic model is the contract. When it's right, your data is legible to whatever consumes it - a human analyst now, a copilot or agent as those mature.
Get the contract right and the readiness follows. That foundational work is real, deliverable today, and where the firm's craft is deepest.
Start with a low-risk diagnostic. Then the foundational work that produces value on day one - no dependency on preview tooling.
A structured, scored assessment of how ready your Fabric estate is for AI-driven consumption. It shows precisely why copilots underperform on your data, and hands you a prioritised remediation roadmap mapped to the four service lines below.
Low commitment. High clarity. The front door to everything else.Complete, described, synonym-rich, well-governed semantic models - and a prepared ontology layer - run through all four lines. So when Fabric Copilot and Data Agents mature, your data is already legible to them. We prepare the foundation; we don't claim to deploy production agents today.
We don't lead with a platform rebuild. We lead with a diagnostic - a clear read on how AI-ready your data actually is, and the shortest path to closing the gap.
Scored across semantic completeness, metadata completeness, and governance fitness - a prioritised picture of exactly where you stand.
The front doorThe resilient architecture and governed semantic models that make data legible - built for Direct Lake and the way Fabric actually consumes it.
The substanceOperational governance with Microsoft Purview, and a foundation prepared so readiness holds as the AI tooling matures around it.
The durabilityAI enablement follows as a later, optional stage - when the tooling is production-ready, not before.
Most firms configure platforms. We make the data underneath them legible - the part that actually determines whether analytics is trusted and AI works.
Every decision traces to a documented architecture principle. A codified body of standards and design records sits behind the work - discipline you can audit.
One senior practitioner owns and directs your engagement end to end. Agentic AI accelerates the production work behind it - so you get a team's throughput with one accountable owner. Assistance, not autopilot.
A quarter-century specialising in business intelligence, data warehousing, and enterprise analytics - spanning vendor-side roles at Crystal Decisions and Business Objects, principal consulting across Queensland government and health, and global BI leadership in enterprise.
Orchestiris is deliberately small and deeply specialised. No layers, no hand-offs - the person who scopes your engagement is the person who builds it. The judgement and the accountability are human; agentic AI accelerates the production work behind them - authoring, refactoring, scaffolding. It's how one senior practitioner delivers enterprise-team depth: assistance, not an autonomous agent workforce.
The focus is narrow on purpose: mid-sized Australian and APAC organisations - larger businesses, government agencies, and major not-for-profits running or planning a Microsoft Fabric platform, under real pressure to prove their data investment pays off.
Every engagement is anchored to a standards-led method: principles and decisions written down before implementation, so the foundation we build is one you can audit, extend, and trust.
A short, honest diagnostic - not a sales process. You'll come away with a clear read on where your data stands and the shortest path forward, whether or not we work together.