This shows how AI-assisted work is reviewed, approved, and delivered with full human accountability.
Client trust isn’t a vibe — it’s a process.
At Rune Digital, I use AI to accelerate delivery, not replace accountability.
• Client data is isolated to each project
• Data is never reused or trained across clients
• All outputs are human-reviewed and signed off
This is documented,...
A documented quality and safety framework outlining how AI-assisted work is reviewed, approved, and delivered with full human accountability.
This playbook governs data cleanup, validation, and documentation workflows to ensure accuracy, trust, and predictable outcomes.
I’ve started offering a Data Cleaning & Quality Assurance service focused on getting datasets delivery-ready before analysis or reporting.
My process covers:
• Schema & structural integrity
• Formatting and normalization
• Cross-field consistency checks
• Outlier and null...
Documented pre-delivery QA checklist for data processing work.
This checklist reflects the process I use to validate datasets before handoff, including:
- Structural integrity checks (keys, schema, required fields)
- Formatting and normalization
- Accuracy and consistency review
-...
