Case Study 02
AI-Assisted Publishing Dashboard
A dashboard that turns Korean skincare rankings into approved, publish-ready product pages through an AI-assisted workflow.
Overview
Seoullective introduces Korean skincare to a US audience. The dashboard streamlines their weekly publishing pipeline: it collects top rankings, cross-checks product details, distills product research into quality PDP content, and processes Korean reviews to reveal exactly what people are saying.
The Problem
Authentic Korean skincare data exists in high volume, but it's linguistically and structurally hard to reach. Hundreds of reviews, fragmented ranking lists, new products surfacing every week, all requiring scraping, translation, and analysis from scratch.
But the manual grind went beyond just effort. Disconnected steps repeated weekly meant quality was always at risk of slipping. And even then, the real challenge remained: distilling raw data into the specific, actionable keywords US shoppers need to make a decision.
The Approach
Automate the research. Protect the editorial call.
The system splits work into three clear layers: deterministic code handles data collection and scoring, LLMs synthesize content and interpret reviews, and one manual checkpoint preserves editorial judgment. AI does the heavy lifting. Humans make the final call.
Code
Deterministic logic. No ambiguity, no hallucination risk.
Data collectionScoringName matching
AI
Contextual interpretation. Where LLMs materially improve output.
PDP content synthesisReview analysis
Human
Editorial judgment. The one decision that can’t be automated.
Approve Top 10Curate imagery
Code
Collect, score, and enrich
Pull rankings from multiple sources, normalize product names, score candidates with deterministic logic, and verify seller availability, all before any AI touches the data.
AI
Generate content and analyze reviews
Synthesize ingredient data into publish-ready PDP content, and distill hundreds of Korean reviews into keywords and skin-type suitability signals.
Human
Approve
The one editorial decision that stays in human hands. Everything downstream moves fast because this gate moved slow.
The Results
Fewer steps, no compromises
Delta-processing skips previously enriched products automatically. The weekly workload shifted from processing every product to just the new entrants, without compromising editorial quality.
Delta processing
Previously enriched products are automatically skipped. Only new entrants require full processing.
Focused human effort
Editorial energy goes to the Top 10 decision, not repetitive data work.
Consistent output quality
AI-generated content follows the same structure every run. No variance from manual fatigue.
Takeaways
A workflow does not need to be fully autonomous to be competitive. It needs to automate the right work and protect the right decisions.
One well-placed human checkpoint is often stronger than many vague claims of full automation.
The most credible AI systems are explicit about where they rely on code, where they rely on models, and where they still rely on human judgment.
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