Most Power BI tenants grow report by report, until nobody knows which number is right or what the capacity bill actually buys. We review your workspaces, semantic models and capacity, then lay out what to fix, what Fabric should take over and what Copilot needs to give useful answers.
We go through your workspaces, semantic models, reports, gateways and capacity metrics. Then we tell you plainly what works and what needs attention.
Which reports need a rebuild, which can go, and what should self-service look like a year from now? We turn the answers into a phased plan. When that plan calls for new models and reports, the people who wrote it can build them through BI development.
Fabric puts lakehouses, warehouses, pipelines and real-time analytics around Power BI. Do you need all of it? Usually a part is enough. We check which workloads would gain from Fabric and which are fine where they are. Since the answer depends on your whole platform, this work often runs alongside data engineering consulting.
Microsoft no longer sells new Power BI Premium P SKUs, and each one ends with its agreement term. The recommended path is the matching Fabric F SKU. After the subscription ends, there’s a 30-day grace period. From day 31 operations are throttled, and from day 91 they’re rejected. We plan the move so your reports keep running without a pause.
Copilot in Power BI runs on any paid Fabric capacity from F2. Whether its answers are any good depends on your semantic models. And many companies aren’t there yet: Gartner found that 63% of organizations either lack AI-ready data practices or aren’t sure they have them.
Slow reports usually trace back to the model. We review your star schema, relationships, DAX measures and refresh setup, and give your team specific changes to make.
Moving from Tableau, Qlik, SSRS or Excel? We count what you have, sort it by complexity and tell you what’s worth moving at all. Still choosing between platforms? Tableau consulting gives you the other side of the comparison.
As Power BI spreads across a company, workspaces multiply and nobody knows which report is the official one. We set up rules that keep things in order and still let people move fast.
Reports pay off when people actually use them. We help you set up a small Center of Excellence, write standards for report builders and train each role.
We start with the decisions your reports should support, the people who use them and any deadline you’re facing, like a Premium renewal. You give us read access to the tenant, and we take it from there.
We review models, reports, gateways, capacity metrics and usage. Along the way, we also check whether Power BI is the right tool for everything you need it to do.
You get findings ranked by impact, a target setup and a phased plan with effort estimates. We walk your team through it and adjust it before it’s final.
Your admins and analysts make the changes. We review model changes before they reach production, join a weekly check-in and step in on the tricky calls. Prefer to hand the build over? Our BI engineers can take it.
Two months after go-live, we rerun Performance Analyzer and capacity metrics on the same reports and compare them with the baseline. Repeat the check each quarter or after a big jump in users.
About two weeks inside your tenant, ending with a ranked findings report. Best when something feels off and nobody has measured it yet.
Built around a deadline or a big decision: a Premium agreement running out, a Fabric rollout or a move from Tableau or SSRS.
A set block of hours each month with a senior Power BI architect your team can call on.
A consultant joins your BI team for a set period and works through the backlog with your analysts.
Our advice stays the same whichever SKU or tool you end up buying. Azure, AWS, Google Cloud or on-premise, the plan picks what fits your users, even when that means a smaller capacity.
Slow refreshes and wrong totals often start in the warehouse. Our consultants have built those layers on data engineering projects, so each fix lands where the problem really is.
Our team includes five Claude certified architects, credentialed by Anthropic. When AI agents join your reporting, they set up access rules and governed models from the start.
Once a roadmap turns into build work, AI-assisted development shortens delivery by up to 50% and cuts bugs by up to 20%. Every release passes senior review before it ships.
ISO 27001:2022 certification, GDPR coverage and HIPAA-ready processes shape how we handle your tenant. You decide who on our side gets access, and for how long.
An assessment usually follows a specific trigger: a board report with the wrong number, a capacity that throttles every Monday morning, or a Premium renewal notice.
Five areas get reviewed:
The output is a findings document ranked by business impact, a 90-day plan and a set of fixes your report builders can ship within the first week.
The admin portal’s usage metrics show views per report. Sort them by the last 90 days: every report at zero is a candidate to archive, and that count gives a first measure of clutter. Without admin rights, usage data is simply the first thing the assessment pulls.
Three numbers shape most estimates: how many semantic models carry your business reporting, how many capacities you run, and how many teams depend on the reports.
Scope adds the rest. A tenant review on its own is the smallest piece of work. Sizing Fabric capacity, planning a Premium exit or preparing models for Copilot each add interviews, testing and documentation.
Before asking for a quote, pull two figures from the admin portal:
Those two figures narrow the estimate quickly. If the admin portal isn’t open to you, an intro call covers the same ground in half an hour.
Assessments run at a fixed price, and the findings belong to you whatever you decide next. To get a range for your tenant, contact us with the figures above.
Early findings come within the first week. They cover reports nobody opens, refreshes that keep failing and capacity peaks.
The full roadmap for a single tenant lands within 14 days of kickoff. A Premium exit plan or a setup with several capacities takes four to eight weeks, mostly because of the testing involved.
Two things shorten the start:
Without the app, setup takes about an hour during the first session, and capacity history builds from there.
Across all Relevant projects, 99% are delivered on time.
Microsoft no longer sells new Power BI Premium P SKUs. Each one ends with its agreement term, and the recommended path is the matching Fabric F SKU. After the subscription ends, you get a 30-day grace period. From day 31 operations are throttled, and from day 91 they’re rejected. Pro, PPU and Embedded aren’t affected. Microsoft covers the details in its Premium migration guide.
A smooth move usually takes three steps:
Check today: in the admin portal, open Capacity settings, note whether each capacity is P or F, and find the agreement end date. If the end date isn’t visible there, the assessment finds it with your Microsoft licensing contact and builds the timeline backward from it.
The people using your reports decide the right capacity. Builders and view-only users count very differently in each option:
Microsoft Fabric isn’t required to use Power BI. Fabric capacity becomes worth it when you want a lakehouse or warehouse next to your reports, Direct Lake models, Copilot, or a large view-only audience.
The right SKU comes from your real usage: how many people build reports, how many only view them, how big the models are and when load peaks. During the assessment, we pull those numbers from your tenant and the Fabric Capacity Metrics app, then recommend a capacity size with headroom for the growth you have planned.
Copilot in Power BI runs on any paid Fabric capacity from F2, once an admin turns Copilot on in tenant settings. The licensing part is quick. Preparing the semantic models takes most of the effort.
Copilot answers from your models, so it needs:
Many companies aren’t there yet: Gartner found that 63% of organizations either lack AI-ready data practices or aren’t sure they have them.
Check one model today. Open your most-used semantic model and see whether its top ten measures have descriptions and plain names. If most don’t, start there. If you can’t tell which models matter most, the Copilot readiness review ranks them and lists the fixes for each.
Start with what the business actually opens. Inherited tenants carry years of copies: report versions ending in _v2 or _final, production content sitting in personal workspaces, and gateways nobody can log into.
The assessment maps every report back to its semantic model and data source, then marks each one as keep, repair or remove. Production content moves out of personal workspaces. Gateway credentials get documented and rotated. The models you keep get an owner and a short note on what they calculate.
A five-minute check: search the tenant for report names that end in v2, copy or final. A long list means duplicate logic, and duplicate logic is where totals start to disagree. If searching the tenant isn’t possible for you, the first day of the assessment produces that list, with no preparation needed.
Look at the next six months of work. When most items are decisions, an advisor is enough. Choosing a Fabric SKU, setting governance rules or reviewing a vendor’s models comes down to senior judgment, and one experienced architect covers it.
A full team makes sense when most items are builds: dozens of reports to rebuild after a migration, new semantic models for a new business unit, or embedded analytics in a product. That work goes to BI development, where the consultants who wrote your plan stay involved.
A simple sort helps. List each planned item and mark it D for decision or B for build. Mostly D points to monthly architect hours. Mostly B points to a delivery team. If the list isn’t written yet, the assessment drafts it with you.
Licenses are the smallest part of a BI migration. Report logic, user habits and trained analysts all move with it, so the gain has to outweigh the rebuild.
A move to Power BI usually makes sense when:
The deciding facts come from your running environment: how many reports people open each month, how complex the calculations are, and which data sources already sit in Azure. Once we’ve seen those systems in operation, you get a recommendation on the path with the lower cost and the smaller disruption, plus the effort each one takes. A good share of old reports can often be retired, which shrinks the move.
Start today by counting the reports opened in the last 90 days on your current tool. That count sets the real size of the move. If the count is hard to pull, the migration assessment produces it, and the comparison from the other side runs through Tableau consulting.
96% of our people stay with us, and 99% of projects finish on time. The consultant who assesses your tenant is very likely the one checking performance two months later.
Start with a short assessment of your Power BI setup, and you’ll know what to fix first.
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