Challenge
- Manual content production could not keep pace with catalog growth.
- Metadata quality was inconsistent across product groups.
- Publishing cycles were slow and expensive, limiting SEO velocity.
Approach
- Built a controlled LLM pipeline for first-draft generation by product type.
- Added QA gates for brand tone, factual consistency, and SEO structure.
- Automated metadata generation with entity-focused keyword variants.
- Connected publish workflow to indexation monitoring dashboards.
- Iterated templates with weekly performance feedback loops.
Results Timeline
| Metric | Month 0 | Month 6 | Month 12 |
|---|---|---|---|
| Indexed Product Pages Index | 100 | 210 | 420 |
| Content Cost per SKU (EUR) | 1.00 | 0.52 | 0.35 |
| Conversion Rate Index | 100 | 118 | 142 |
| Publication Velocity (pages/week) | 320 | 1,100 | 1,850 |
What Moved the Needle
- +320% indexed page growth at stable content quality.
- -65% content production cost per SKU.
- Faster go-to-market for new catalog segments.
Key Takeaway
AI content systems work when constrained by SEO templates, validation layers, and clear quality thresholds.
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