We design semantic models with one agreed definition per measure, build the lakehouses and warehouses that feed them, and implement the governance that keeps both auditable. From the first source system to the published report.
Illustration, not a client report. Every figure on a real one traces back to a named owner and a written definition.
Two reports disagree and nobody can say which is right
The same measure defined in four places
The overnight refresh fails and someone re-runs it by hand
No ownership, no alerting, no replayable load
A legacy BI renewal is due and the price no longer makes sense
Licensing bought per seat for an estate that has changed shape
Copilot was switched on and gives inconsistent answers
Metadata too thin for the model to disambiguate
Semantic modelling, DAX and report delivery with one agreed definition per measure.
Read moreLakehouse architecture sized against measured consumption rather than headcount.
Read moreAzure pipelines, warehousing and lakehouse engineering built to run unattended.
Read moreStructured migration from legacy BI platforms, with rationalisation and a reconciled cutover.
Read moreOwnership, cataloguing, classification and access evidence, implemented in the tools teams already use.
Read moreCopilot readiness, custom assistants and forecasting, assessed against a fixed evaluation set.
Read moreMonitoring, incident response and change delivery for Power BI, Fabric and Azure platforms.
Read moreDescribe the symptom instead
Describe the symptom and we will tell you which practice it belongs to, or that none of them do.
Describe the problemA four-billion-row warehouse rebuilt on Azure Synapse, with Power BI served through an API layer rather than competing with the transactional system for resources.
Read the caseAn inventory and a rationalisation decision per report before any rebuild, business logic moved out of the BI tool into Azure Synapse, and parity proved report by report before each wave went live.
Read the caseA commercial platform where the pipeline, the order, the invoice and the executive KPI read from one record, so that nobody reconciles two systems by hand to find out what was sold.
Read the caseWe do not build a revenue report until the business has agreed what revenue means, who owns that definition and where the exceptions sit. Most reporting disputes turn out to be definitional rather than technical.
We propose the least expensive design that satisfies the requirement, and we say so when a capacity, a lakehouse or a migration is not justified. Overbuilt platforms are easier to sell and considerably harder to operate.
Performance work is reported with before and after traces. Migrations are signed off against reconciliation evidence. AI features are assessed against a fixed evaluation set. Where something cannot be measured, we describe it as a judgement rather than a result.
Architecture decisions, measure definitions and runbooks are written as the work proceeds and kept in your own systems, rather than assembled in a final week before invoicing.
The work is complete when your team can change it without us. We are glad to continue on a small retainer, but the platform should not depend on our presence to keep running.
Several conversations a quarter end with a recommendation that is not a project: a licensing change, a process fix, or an internal hire. We would rather give that answer than sell work that will not pay for itself.
Three to eight weeks from first conversation….
English.
How buying worksEURFour to twelve weeks for an Irish-headquarte….
English.
How buying worksEURSix to sixteen weeks in the private sector.
German for the contract, the AVV and the acceptance documentation, even where the delivery team works in English.
How buying worksCHFEight to twenty weeks.
German, French or Italian depending on the canton; English is common in banking, pharmaceuticals and international groups.
How buying worksEURSix to fourteen weeks commercially.
Dutch in Flanders, French in Wallonia and Brussels, English with international groups. Assume at least two.
How buying worksWhy Power BI Copilot produces unreliable answers and what actually fixes it: model consolidation, field descriptions, synonyms, measure coverage, and an evaluation set that makes accuracy measurable.
The honest comparison of Power BI Pro, Premium Per User and Fabric capacity: what each one actually includes, where the break-even point sits, and the four ways organisations overspend.
How to design a Power BI semantic model that performs and survives: star schema discipline, conformed dimensions, slowly changing dimensions, role-playing dates, and the five modelling mistakes that cause most support calls.
Practical data governance for analytics estates in Lucerne: what the Federal Data Protection and Information Commissioner (FDPIC / EDÖB) expects under the revised Federal Act on Data Protection (revFADP / revDSG), in force since September 2023, the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
Practical data governance for analytics estates in Würzburg: what the federal commissioner (BfDI) and the Land data protection authorities expects under the EU GDPR and the Bundesdatenschutzgesetz (BDSG), the sector rules that bite locally, Microsoft Purview in practice, and a 90-day plan.
A practical guide to Power BI consultancy in Karlsruhe: the definition traps in software and technology and electronics and semiconductors, honest licensing arithmetic, hire-versus-engage costs, and the compliance position under the EU GDPR and the Bundesdatenschutzgesetz (BDSG).
We design and build the data layer that reporting and AI sit on: semantic models in Power BI, lakehouses and warehouses in Microsoft Fabric and Azure, the pipelines that feed them, and the governance that keeps them trustworthy. We also migrate legacy BI estates onto the Microsoft stack and operate platforms as a managed service. We are a consultancy, not a software vendor. There is no product you have to buy with us.
With a short paid discovery, typically two to three weeks, producing an architecture recommendation, a delivery plan and a cost estimate based on your actual data rather than an average. You keep that output whether or not you continue. We avoid open-ended time and materials for first engagements because neither side can judge value without a defined scope.
Discovery is a fixed fee in the low five figures. Build work is priced by project size rather than by report count: a small project (one or two sources, one business area, three to five reports) runs £15,000 to £40,000 and takes three to six weeks. A medium one, with three to six sources, several business areas and real governance, runs £40,000 to £120,000 over six to twelve weeks. A large platform with ERP and CRM integration, a dedicated data layer and formal governance runs £120,000 to £400,000+ across three to six months. The same figures apply in euro and Swiss francs in our other markets. Managed service starts at £3,000 a month for a small estate. We give a written estimate range before discovery and a fixed price after it.
The United Kingdom, Ireland, Germany, Switzerland and Belgium. Delivery is predominantly remote with on-site workshops at the points where being in the room matters, definition workshops, go-live and stakeholder sessions. We work in English and German, and we handle Dutch and French-language stakeholder sessions through our network where needed.
Work with them, in almost every case. The most common pattern is an internal analytics team that owns business engagement, with us covering platform engineering, architecture and the work that needs specialist depth. We build in the open, document as we go, and run enablement during delivery rather than in a final week.
We work exclusively on the Microsoft data stack and maintain certification across Power BI, Fabric and Azure data engineering. We are deliberately independent on licensing: we have no incentive to recommend a larger capacity than you need, and we will tell you when a cheaper licensing model fits your reader population.
For a well-defined first use case with accessible sources, six to eight weeks from kick-off to a production report is realistic. The variable is almost never build speed. It is source access, data quality and how long it takes your organisation to agree a KPI definition. We front-load both so the surprises happen in week two rather than week ten.
Say so on the call. A fair share of the conversations we have end with a recommendation that is not a project: a licensing change, a process fix, a report retirement, or hiring one internal analyst instead of engaging a consultancy. We would rather be the people who told you that than the people who sold you a platform.
A 45-minute working session with an engineer rather than a sales call. You describe the problem; we set out what we would build, in what order, and what it is likely to cost.
Answered within one working day, by the engineer who would do the work