Microsoft Fabric Lakehouse and Power BI, built for AI.
A trustworthy agent needs trustworthy data. We build medallion Lakehouses in Microsoft Fabric that feed both your dashboards and your AI, from one governed source.
- First release
- 4–8 weeks
- Ideal for
- Teams with data spread across an EHR, ERP, CRM and spreadsheets
- Stack
- Fabric, PySpark, Dataflow Gen2, Power BI, DAX, Azure AI Search
- Pattern
- Bronze → Silver → Gold, star schema
The challenge
AI on messy data gives messy answers
When the same metric means different things in different reports, an AI assistant will repeat the confusion with confidence.
We build one governed data layer with clear definitions, then point both Power BI and your AI at it.
Scope
What we build
- Medallion LakehouseRaw Bronze copies, cleaned Silver tables and business-ready Gold models in Fabric.
- PipelinesPySpark notebooks and Dataflow Gen2 jobs with tests, scheduling and alerts.
- Star-schema modelsFacts and dimensions for visits, orders, claims, scheduling or sales.
- Power BI with Q&AReports and natural-language Q&A tuned with synonyms and linguistic schemas.
- AI groundingGold tables exposed to agents through search indexes or MCP tools.
How it runs
Clear stages. You decide at the end of each.
Source review
Inventory systems, tables and metric definitions.
1–2 weeksLakehouse
Bronze and Silver layers with pipelines.
2–4 weeksGold and BI
Models and first reports.
2–3 weeksAI layer
Connect agents to Gold data.
2–4 weeksQuestions
What teams ask us about this.
We already use Synapse or Databricks. Can you help?
Yes. The medallion pattern is the same, and we can work in your current platform or plan a move to Fabric.
Can business users ask questions in plain English?
Yes. Power BI Q&A and AI agents both work far better on a clean Gold model with agreed definitions.
How do you handle sensitive data?
Row-level security, masking in lower layers and access through your Entra ID groups.
Related
Start a project
Tell us what you want AI to do. We'll tell you if it can.
A 30-minute call with an engineer. You leave with a straight answer on feasibility, rough cost and the first step.