Data intelligence
Power BI that earns its place in the boardroom.
We design and ship Power BI suites that hold up under exec scrutiny, and we diagnose existing tenants that have stopped paying their way.
The work
From "the numbers are stale" to live decision support.
Most BI engagements we walk into aren't failing because the tooling is bad. They're failing because nobody has connected the data layer cleanly to the source systems, the semantic model is doing implicit conversions in three different places, and refreshes break on every long weekend because nobody documented the gateway.
We start by getting the data layer right: Azure SQL or Fabric Warehouse, modelled properly, refreshing reliably. Then we build the executive surface on top: P&L, gross margin, PPV, OEE, demand variance. Each report tied to a decision somebody actually makes.
We have done this for ASX-adjacent FMCG manufacturers, regional distributors, and Australian food production sites. Our diagnostic engagements have rescued tenants where two prior consultancies had given up.
Capabilities
What we build inside this practice.
Executive reporting suite
Single-pane-of-glass P&L, revenue, margin, and category mix, refreshing every 15 minutes off the live ERP. Replaces month-end manual cycles.
PPV / MPV / variance reporting
Purchase price variance, manufacturing price variance, and budget vs actual analytics surfaced by product line, supplier, and category. The reports your finance team actually wants.
Demand planning & forecasting
Forecast-vs-actual trend lines with statistical methods (Holt-Winters, ARIMA) and ML where the workload justifies it (Microsoft Fabric AutoML, Azure ML). We don't invent demand from line-of-best-fit theatre.
Predictive analytics & anomaly detection
Power BI built-in anomaly detection for spotting unusual patterns in PPV, demand, OEE, supplier prices, or compliance KPIs. Custom Azure ML models where the workload earns it. Statistical drift alerts so finance hears about month-end anomalies before they close the books.
AI-native dashboards (conversational BI)
Microsoft Fabric Copilot and Azure OpenAI Service layered onto your Power BI suite, so exec teams ask questions in plain English ("what was PPV last quarter for category X?") and the dashboard answers with the right visual. We build the data layer that makes this reliable rather than only demo-friendly.
Automated alert systems
Power Automate flows that trigger from BI thresholds: out-of-spec PPV, demand collapse, low cold-chain compliance. Email, Teams, SMS, ServiceNow, with escalation chains and acknowledge-or-escalate logic where the stakes justify it.
BI system diagnostics
Audit of an existing Power BI tenant. Dataset and gateway health, refresh failure root cause, semantic model assessment, RLS review, capacity sizing. Comes with a written remediation plan.
Custom Power BI visuals
When the marketplace visuals don't cut it, bespoke React + D3 visuals packaged as PBIVIZ. Used sparingly; usually for plant-floor or operations control views.
Stack
What we work with.
We are deeply specialised on the Microsoft data stack, with Power BI as the centre of gravity, but we model the warehouse layer cleanly so the same data is reusable for Python, R, Tableau, or whatever comes next.
- Power BI Desktop, Service, Premium / Fabric capacity
- Microsoft Fabric Copilot · Azure OpenAI Service (for conversational BI + AI-native features)
- Azure ML · Fabric AutoML (predictive analytics, anomaly detection, forecasting)
- Azure SQL · SQL Server · Synapse · Fabric Warehouse · TimescaleDB (for high-frequency telemetry)
- Azure Data Factory · dbt · Python ETL
- DAX · Power Query (M) · T-SQL
- Power Automate · Power Apps (where it earns its keep)
- Source systems: Pronto · Infor LX · SAP B1 · PCG · NetSuite · LOMA · Ishida · custom
Start with discovery
Two weeks. We audit what you already have. You see what it would take.
Before any dashboard gets built, we run a fixed-scope discovery: interviews with the people who use the reports, an audit of the source systems, and a written set of KPI definitions everyone actually agrees on.
Most Power BI work fails at the definition layer, not the visual layer. Two people mean different things by 'margin', the semantic model picks one, and finance stops trusting the dashboard six weeks after launch. Discovery is where that gets settled.
You get the document either way. If the data you'd need is not reachable, or the reporting you want is cheaper to solve another way, we'll say so before you commit to a build.
FAQ
Frequently asked
Do you build Power BI from scratch, or fix what's already there?
Both. Greenfield Power BI implementations on Azure SQL / Fabric / SQL Server are our core work, but a lot of engagements start with us diagnosing an existing tenant: broken refreshes, datasets nobody understands, semantic models that don't match the source data. We charge separately for that diagnostic so you only pay for build once we both agree on the scope.
We're not on Microsoft today. Can you still help?
Yes. We've shipped reporting on top of SAP B1, Pronto, Infor LX, PCG, NetSuite, and bespoke databases. Power BI is the most common front-end because of its licence economics and AU enterprise penetration, but the data layer underneath can be anything that exposes SQL or an API.
What does a typical engagement look like?
Two-week discovery (interviews, source-system audit, KPI definitions, success criteria). Then a fixed-scope build sprint of 4 to 8 weeks for the first dashboard set, with weekly demos. After go-live we offer a retainer for enhancements and break-fix, or hand it cleanly to your in-house team, whichever you prefer.
Can you work with our existing BI team?
Often. We've come in as a senior pair-of-hands when the in-house team is stretched, or as a specialist for things like DAX optimisation, row-level security architecture, and semantic model refactors. We don't need to own the work; we just need to be useful.
How do you price it?
Fixed-price for defined deliverables (discovery, dashboard build, integrations). Time-and-materials for ongoing enhancement or retainer support. We don't bill by the hour for things that should be quoted, and we don't run open-ended consultancy.
Can our exec team ask the dashboards questions in plain English?
Yes. Microsoft Fabric Copilot and Azure OpenAI Service have made conversational BI genuinely workable in 2026. We build the semantic model, the data layer, and the Copilot integration so the natural-language layer is reliable (not a demo trick that breaks on the third question). The trick is the underlying model quality, not the AI layer. Get the data layer right and the Copilot answers correctly.
What about predictive analytics and anomaly detection: real, or hype?
Real for specific use cases, hype for many others. Where it works: anomaly detection on PPV / OEE / supplier prices (Power BI's built-in capability is genuinely useful), demand forecasting beyond Holt-Winters using Fabric AutoML or Azure ML, statistical drift detection on long-term KPI trends. Where it doesn't: anything where the underlying data is too sparse, too noisy, or the business decision is more nuanced than "flag the outlier". We're honest about which it is when we scope.
Related
Other things we do
ERP & Systems Integration
Integration across the systems that run your business, plus custom-built connectors for anything that doesn't ship with one. We bridge the platforms your team already relies on.
Explore Product EngineeringSaaS & Product Engineering
We architect, build, and ship SaaS platforms, custom web apps, and mobile tools. Clean code, fixed-scope sprints, and a delivery pace that doesn't drag.
Explore Industrial TechIndustrial & Operational
Software for the factory floor and the teams who run it: checkweigher monitoring, HACCP compliance, manufacturing analytics, and rostering built around how your people actually work.
Explore
Need clarity in your data?
Tell us what you wish you could see, or which reporting cycle is eating your week. We'll tell you what it would take to fix.