Built once. Used at scale.
Trusted by the people who decide.
MDI builds Power BI on Microsoft Fabric Direct Lake. Governed semantic models per domain, Power BI Copilot, Row-Level Security at the model layer, deployment pipelines — for mid-market manufacturers, FMCG and supply chain. First production semantic model in 6 weeks.
The Problem
Patterns we see in every engagement
Most Power BI projects do not fail at the dashboard. They fail at the gateway. They fail at the semantic model that nobody owns. They fail when the refresh times out at 2am and the morning leadership review goes back to spreadsheets. These are the patterns we close.
Import models that have outgrown the refresh window.
Your fact table crossed 50 million rows. The overnight refresh now takes 4 hours. Wednesday morning's report is yesterday's data because the refresh failed at 6am. Operations stops trusting the dashboard and goes back to ERP exports. Direct Lake on Fabric removes the refresh entirely — same data, zero hydration cost.
27 versions of YTD because everyone built their own measure.
Three plant managers, three different OEE definitions, three different YTD calculations. The leadership review argues about whose number is right. Governed semantic models with calculation groups end this — one measure, named for the business term, owned by a person.
Copilot enabled on a workspace nobody cleaned up first.
Power BI Copilot now works in production for plant-manager-grade questions. It also reflects back whatever mess sits in the semantic model. Garbage measures lead to confident wrong answers. We clean the model before we enable Copilot — not after.
Tableau or Qlik estate that needs to move to Power BI.
Your enterprise standard shifted. The Tableau renewal is up. The migration to Power BI looks straightforward until you start mapping 400 dashboards across teams, RLS rules, and embedded reports. We have moved several estates — we know which patterns transfer cleanly and which need rebuilding.
What we build
What we build for Power BI buyers
Six engagement shapes. Each one named for a real outcome an Operations Director or CFO would describe.
Direct Lake migration from Import
Replaces
The 4-hour overnight refresh window that breaks twice a month and makes morning standups argue about whose number is right.
- Audit existing Import models for unsupported DAX patterns before migration commits
- Rebuild semantic models on Delta Parquet in OneLake — no refresh, no hydration penalty
- Parallel run new vs old for 2 weeks before cutover so business has confidence in the new number
- Fabric F-SKU capacity sized to actual workload — not guessed at procurement time
Refresh window removed. Query latency within 10–15% of Import. Capacity bill typically drops 20–35%.
Enterprise semantic model build per domain
Replaces
27 user-built models with conflicting OEE / OTIF / Fill Rate definitions that nobody can reconcile in a leadership review.
- One governed semantic model per business domain — Manufacturing, Supply Chain, Customer, Finance
- Measures named for the business term, not the source column. RLS at model layer, not bolted on per report
- Calculation groups for time intelligence — stop maintaining 27 versions of YTD vs PYTD
- Owned by a named person on your team — not orphaned the day the contractor leaves
One model. One number. Leadership reviews stop arguing about whose calculation is right.
Power BI Copilot enablement (done properly)
Replaces
The analyst inbox queue — plant managers emailing because they could not find OEE on Line 3 yesterday and the dashboard timed out anyway.
- Harden the semantic model first — Copilot reflects garbage measures back as confident wrong answers
- Document every measure with synonyms and example questions so Copilot maps natural language to your business terms
- Scope Copilot to governed workspaces — block it from running over un-curated departmental models
- Train plant managers, supply chain leads and CFO on what Copilot is good at and what it is not
Plant managers ask questions in plain English at the morning huddle. Analyst inbox queue shrinks 40–60%.
Row-Level Security for multi-plant and multi-region
Replaces
Per-report RLS hacks that break every time someone publishes a new copy of the dashboard.
- RLS at the semantic model layer — every report inherits without per-report configuration
- Plant-level, region-level, customer-account-level security models
- Dynamic RLS via Entra ID — joiner-mover-leaver handled automatically through user attributes
- Audited regularly so security drift is visible, not silent
One model, every report secured. New report inherits the rules. No more spreadsheet-based access matrices.
Deployment pipelines: Dev → Test → Prod
Replaces
The Power BI workspace where the report that is correct lives on someone's laptop and the production version has been broken since the Tuesday update.
- Power BI Deployment Pipelines with Git integration for semantic models and reports
- Branch-per-feature workflow so a broken measure does not take production down
- Automated checks on data quality and measure performance before promotion to production
- Audit trail of who changed what and when — visible in seconds, not searched through Teams chats
Production stability returns. Changes are deliberate, reviewable, and revertible.
Performance tuning for slow Power BI
Replaces
The 40-second visual that everyone has accepted as 'how Power BI works' but really represents four bad DAX measures and a missing aggregation table.
- Performance audit with DAX Studio and Tabular Editor — name the actual bottleneck, not the symptom
- DAX measure refactoring — we have taken 40-second visuals to 1.2 seconds by rewriting four measures
- Aggregation tables and composite models where Direct Query is the right shape
- Capacity load analysis — sometimes the answer is workload distribution, not more SKU
Slow reports become usable. We have taken 4-hour refreshes to 12 minutes by fixing the model, not the gateway.
How we work
From audit to first governed model in 6 weeks
Discover → Prototype → Deploy → Expand. Same shape for every Power BI engagement. First production-grade domain in 6 weeks, not 6 months.
01
Discover — audit the Power BI estate
We pull 90 days of usage logs from Power BI's own activity API. The 20 reports driving 80% of views, the orphans nobody opens, the gateways that fail. We score every semantic model for governance, naming, and ownership. The output is a ranked list — what to rebuild first, what to archive, what to leave.
02
Prototype — one production semantic model on Direct Lake
Two weeks to build the chosen domain on Direct Lake with two production-grade reports. Parallel run against the existing Import model. We prove the latency, the capacity cost, and the migration effort with real data — not slides.
03
Deploy — domain by domain, owner by owner
Move the domain over. Deployment pipelines, RLS, ownership, Copilot enablement. Train the named model owner. The retainer or sprint engagement continues domain by domain — Manufacturing, then Supply Chain, then Customer, then Finance.
Technology stack
Semantic Modelling
Authoring & UX
Governance
AI & Copilot
Platform
Source Systems
Common questions
What buyers ask us
We have 400 Power BI reports already. Where do we start?
Usage data. Power BI tracks every view via the activity API. We pull 90 days of usage logs, find the 20 reports that drive 80% of the views, and rebuild those on the new governed model first. The other 380 either get archived or auto-converted. We have done this with Power BI estates of 50, 400 and 1,200 reports — the 80/20 pattern holds every time.
Our Power BI sits on SQL Server / Oracle / on-prem. Do we have to move to Fabric?
No. Power BI works with everything. We assess whether the move is worth the migration cost. For some mid-market clients the answer is stay on SQL Server and add a semantic model layer. For others — high data volume, multiple source systems, real-time requirements — Fabric pays back in 9–12 months. We tell you which one you are during Discover.
What about Tableau, Looker, Qlik?
We migrate them. We have moved estates from each. Honest framing: Tableau is better at exploratory data analysis. Power BI is better at governed, scaled, embedded BI. If your buyer is an analyst Tableau may win. If your buyer is an Operations Director or a CFO, Power BI usually wins. We will not promise that migration is fun — but we have run it enough times to know the patterns.
How much does it cost?
Discover is fixed-fee, typically USD 12,000–18,000. Prototype is fixed-fee, typically USD 25,000–45,000 depending on source system count. Steady-state retainer for a Senior Power BI Developer starts at USD 8,000/month for 10 days, USD 14,000/month for 20 days. We quote precisely after the diagnostic.
Can we start in 2 weeks?
For Discover, usually yes. For Prototype, depends on data access readiness on your side — connector credentials, RLS scope, source-system access for our team. We send a Discover prep checklist on day one of the engagement so the gap closes fast.
Ready to move
Book a 30-minute Power BI diagnostic
30 minutes with Amit. No slides. No pitch deck. No obligation to proceed. Most operations leaders leave the call with three things they had not considered.