Open Source
Semantic model validation, structured PBIP/PBIR engineering, and AI-assisted workflow design each isolate a specific discipline so the approach is visible independent of any single client engagement.
Repository Gallery
Open repositories stay visible first so technical proof is immediately inspectable.
powerbi_demo_PBIPxGHCopilot
Automated DAX measure testing built on PBIP, Python, and AI-assisted tooling, designed to catch calculation risk before deployment.
Validation-first BI engineering: systematic DAX risk detection, repeatable test coverage, and CI-ready semantic model workflows.
All public repositories focus on reusable methods and engineering patterns. Client-specific implementations remain private; what you see here reflects the discipline applied across every engagement.
Internal Patterns
These stay visible as evidence of working methods, but not as the first proof block.
PBI_Agent
A deterministic PBIP and PBIR engineering template that enforces validation gates, consistent folder structure, and governance-ready defaults.
Structured engineering habits that transfer across projects: repeatable repository design, clear validation checkpoints, and governance-aware BI delivery.
A2A
A multi-agent framework exploring AI-assisted orchestration across Azure, Power BI, and Databricks BI workflows.
Early-stage but grounded: tests whether multi-agent patterns can reduce manual coordination in real BI delivery pipelines.