A repeatable development framework that bridges low-code platforms, demonstrating the ability for AI to complement the changing workforce landscape by promoting Human In Loop polices for agents.
Quantum Harmony is a working proof-of-concept for a new kind of DevOps system: one where data architecture, semantic clarity, and AI augmentation are integrated from the start—not bolted on after deployment. Building out this flow using LLM models to generate code exploring how much impact AI could have on the low code space.
It’s built to empower:
- Low-code builders with architectural structure
- Devs with semantic clarity before they ever write code
- Teams with an iterative, testable loop for development that doesn’t break production
This system uses Power BI as a semantic sandbox, Copilot as a schema generator, and Dataverse/Power Platform as a delivery layer, forming a feedback loop that validates logic, not just syntax. The Azure platform reduces a lot of overhead auth complexity
Traditional DevOps workflows aren’t built for citizen developers. And most low-code environments move faster than their governance models can keep up.
Quantum Harmony aims to fix that—by treating semantic modeling as the dev environment and making human-supervised AI part of the loop, not just the input.
This framework helps teams:
- Rapidly prototype schema with AI (Copilot)
- Refine logic and relationships in a semantic-first Power BI layer
- Push clean, validated structure back to Dataverse or SQL
- Keep dev and ops moving together, not against each other
QuantumHarmony_SemanticLoop/ │ ├── README.md ← You are here ├── quantum_harmony_model.pbix ← Refined semantic model in Power BI ├── dataverse_schema.json ← Cleaned, deployable schema structure ├── prompts/ │ ├── initial_model_prompt.txt │ └── semantic_feedback_prompt.txt ├── docs/ │ ├── architecture_diagram.png │ └── use_case_summary.md ├── scripts/ │ └── export_to_dataverse_schema.py (optional/for future automation) └── .gitignore
Semantic Loop DevOps is a methodology where data modeling is done in Power BI before writing permanent schema. AI (Copilot) assists in generating early structure, and that structure is refined, tested, and proven semantically before any deployment.
- Generate: Use AI/Copilot to draft a schema
- Ingest: Bring the schema into Power BI for refinement
- Refine: Define relationships, DAX, roles, hierarchies
- Validate: Test the model visually with real or dummy data
- Export: Rebuild the schema in your DB or Dataverse, informed by proven logic
- Loop: Use the refined semantic model to guide AI or developer decisions on the next iteration
To show that DevOps doesn’t have to be gatekeeping. To prove that ops can move at the speed of dev—without breaking things. And to put structure around the role of AI, so it empowers humans instead of bypassing them.
Also, if Microsoft or OpenAI is reading this: Yes. I’ve thought this through. Let’s talk.
- Clone it, drop in your schema drafts
- Use the Power BI file as a sandbox for testing relationships, DAX, structure
- Follow the loop to push logic back into Dataverse or your database
- Extend the prompts or scripts to plug in your own use case