Through the experiments of data engineer Shen Wei, this article explains how to connect outputs from multiple AI tools, automate metrics, and build trust between the UX layer and backend data systems.
Story
Shen Wei is responsible for integrating 12 different AI generation platforms.
Within three months, he realized that key metrics were scattered across Notion, CSV files, and BI dashboards, making it nearly impossible to deliver clear insights.
He created an "AI Data Lab" to consolidate all outputs into a unified dashboard and added validation rules.
Eventually, PMs and operations teams began to truly trust and rely on these insights.
Recommendation:
Use automation scripts to pipe AI tool outputs (via API or exports) into:
Recommendation:
Design a data validation workflow so that whenever metrics look wrong:
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Create “AI Tool Data Cards” for each tool, including: