The platinum layer sits on top of the standard medallion architecture, built specifically to get data ready for LLMs. In this episode, Brick and Landon break down what it takes to build one well.

Landon walks through the two foundations that make a platinum layer work: markdown files that give the LLM business context and call out data gotchas, and a modeling approach that goes further than typical BI denormalization. They discuss why AI models need a single grain of data to avoid summing errors, why report-specific columns need to be stripped out, and why the platinum layer gets materialized nightly instead of served through views.

They also cover the role of MCP servers in this setup, including why Blue Margin builds tightly scoped servers for business users asking direct questions and more open ones for analysts building queries.

If you've been wondering what actually separates a working AI data layer from a frustrating one, this episode covers the fundamentals.

Key Moments:

1:00 — Markdown Files & Context
2:05 — What Happens Without Context
2:58 — Fabric Data Agent Test
3:19 — Modeling for AI
4:25 — The Grain Problem
5:11 — Extreme Denormalization
5:28 — Cleaning Columns & Tables
6:37 — Nightly Materialization
7:43 — Why You Need an MCP Server
8:22 — Two Types of MCP Access

About Blue Margin

Blue Margin is a Microsoft Fabric and Power BI consultancy based in Fort Collins, Colorado. We help mid-market and private equity-backed companies build data platforms that hold up: clean architecture, trustworthy reporting, and dashboards teams actually use. The Dashboard Effect is where we talk through the technical decisions behind that work.

Learn more: https://bluemargin.com