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Back to Case Studies Agriculture/Cannabis
ARMELY Agriculture / Cannabis

Three systems, three exports, and still no answer.

Regulated agricultural operations generate data at every stage of production and sales. When that data lives in Google Sheets, Metrc, and LeafLink with no connection between them, your team spends more time building reports than reading them.

Does this describe your reporting?

  • Production and sales data live in separate systems with no integration
  • Knowing whether you are over- or under-producing requires a manual pull each time
  • Naming and SKU conventions differ across systems, requiring reconciliation by hand
  • Reports are stale before anyone acts on them
  • No consistent threshold system flags production alignment issues
  • Adding a new product category means starting the reporting process over

What Armely built

A regional cultivation and processing company with three facilities and approximately $42M in annual revenue had production data in Google Sheets, sales data in Metrc, and rate-of-sales data in LeafLink. No integration layer connected them.

Armely built a Microsoft Fabric Lakehouse with Medallion architecture across Bronze, Silver, and Gold layers. Automated pipelines now bring data from all three sources into one platform, while five Power BI dashboards give the team a current view of production, sales, and alignment.

A red, yellow, and green indicator system now surfaces production-to-sales alignment at a glance, with drill-through to strain and SKU.

At a glance

BeforeAfter
Production and sales tracked in three separate systems with no integrationUnified Fabric Lakehouse with automated pipelines from all three sources
Production-to-sales alignment required manual pulls and reconciliationAvailable on demand in Power BI with date-range filtering
SKU and naming differences required manual mappingStandardized in the Silver layer and applied consistently across reports
Production alignment was checked manually with no threshold systemRed, yellow, and green indicators by category, with drill-through to strain and SKU
Reports required manual rebuilds and were stale on arrivalScheduled automated refresh keeps five Power BI reports current
No clear path to add new sources without starting overMedallion architecture allows new sources to connect to the existing Lakehouse
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