CASE STUDY / 002 · IN PREPARATION
AI Content
Automation Pipeline.
A local workflow for generating draft content with configurable rules, review stages and execution logs.
Why this project exists
Writing content repeatedly requires a consistent set of rules, multiple review steps and an organised output. The goal of this automation is to reduce repetitive manual work while keeping configuration and quality checks visible.
Current workflow
- Read configurationLoad the content instructions and generation settings.
- Run generationExecute the local script-driven content workflow.
- Review outputApply the existing checking and review stages.
- Record executionCapture timing and log information for troubleshooting.
What comes next
Before publishing the repository, I plan to remove private data, document installation steps, add reproducible sample inputs and analyse the logs. Future work may include a structured event format and dashboard for execution time, success rate and stage-level performance.
Evidence to add
A short demo video, a redacted sample log, architecture diagram, a sanitised example output and measurable before-and-after results where available.