Most organisations do not have a Power BI problem. They have a modelling problem that Power BI made visible. When four reports answer the same question four different ways, the fault is almost never the visual layer. It is that each report carries its own copy of the business logic, built by whoever happened to need it first.
We build the layer underneath: a dimensional semantic model with one definition per measure, tested DAX, row-level security that matches how your organisation actually delegates authority, and a deployment path from development to production that does not involve emailing a PBIX file. Reports then become cheap, because the hard thinking already happened once.
We work at whatever altitude you need, reviewing an estate someone else built, taking a first model from zero, or embedding with an internal team that wants to own it afterwards. We write the documentation on the way through, not as a closing invoice item.
Star-schema modelling with conformed dimensions, role-playing dates, slowly changing dimensions where the business needs history, and composite models only where they earn their complexity. Delivered as a documented model with a measure dictionary.
Measure design that is readable and fast: calculation groups instead of twenty near-identical measures, correct filter-context handling, and tuning against DAX Studio and Performance Analyzer traces rather than guesswork.
Reports built around the decision they support. Explicit information hierarchy, accessible colour, consistent interaction patterns, bookmarks used sparingly, and mobile layouts for the people who read on a phone between meetings.
Row-level and object-level security driven by directory groups, workspace and app design that matches your org chart, and sensitivity labels so an export does not quietly become an uncontrolled copy.
Incremental refresh with correct partitioning, gateway sizing and failover, refresh monitoring with alerting, and a documented runbook naming who fixes what when a refresh fails at 04:00.
A structured audit of an existing tenant: duplicate models, orphaned workspaces, licence waste, broken lineage, undocumented business logic, with a prioritised remediation plan and effort estimates.
We start from decisions, not data sources. Which recurring decisions need numbers, who makes them, on what cadence, and what currently blocks them. This is what keeps the model from becoming a data dump.
Decision and KPI register, with owners and definitions
Profiling of each source system: grain, volumes, change-tracking capability, data quality, refresh windows and access constraints. We flag what is genuinely unusable before anyone promises a dashboard on top of it.
Source profile and data-quality findings
Dimensional model, transformations in the right layer, measures written against the register, and security applied from day one rather than retrofitted.
Deployed semantic model, measure dictionary, security matrix
Reports built with the people who will use them, validated line by line against the source of record until the numbers tie out. Disagreements get resolved and written down, not averaged.
Signed-off reports and a reconciliation record
Workshops for the internal team, documentation in your own wiki, and a 30-day period where we fix what breaks while your team drives.
Runbook, architecture document, recorded enablement sessions
A 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.
BI migration · Business intelligenceAn 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.
Yes, and in most cases that is the cheaper route. We start with an estate review: which models are worth keeping, which should be consolidated into a shared semantic model, and which can be retired because nobody has opened them in six months. Rebuilding everything from scratch is occasionally the right answer, but it should be a conclusion, not a starting assumption.
It depends on how many people read reports versus build them, and whether you need capacity-only features such as larger models, Direct Lake or paginated reports. Pro per user is usually the cheapest option for small reader populations; a capacity SKU becomes cheaper as the reader count grows, and it is the only route to some features. We model your actual user mix and give you the break-even point before you commit, rather than recommending the largest SKU.
Performance is a modelling outcome more than a visual one. Narrow fact tables, integer surrogate keys, no bidirectional relationships unless genuinely required, aggregations where volumes justify them, and measures that avoid row-by-row iteration over large tables. We trace with Performance Analyzer and DAX Studio and publish before-and-after numbers, so the improvement is a measurement rather than a claim.
That is the design goal. Models are built in a source-controlled, scriptable form, naming conventions are consistent and documented, the measure dictionary explains intent and not just syntax, and we run enablement sessions during the build rather than in a rushed final week. Several clients keep us on a small retainer for review and second-line support; plenty do not need to.
Yes. We sign NDAs as a matter of course and prefer, where possible, to work against a masked or subset copy during development. Where production access is required we work inside your tenant under your conditional-access policies, with named accounts and no shared credentials.
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Sector context: Financial services, Manufacturing, Retail, Healthcare, Logistics.
A paid two-week discovery, so the estimate is based on your data rather than on an average
Answered within one working day, by the engineer who would do the work